Imports
In [93]:
# This tells matplotlib not to try opening a new window for each plot.
%matplotlib inline
# Import a bunch of libraries.
import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
# Preprocessing
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import FunctionTransformer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Imputer
# Feature Selection
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.decomposition import PCA
from sklearn.decomposition import KernelPCA
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LassoCV
# Regression
from sklearn.svm import SVR
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsRegressor
from sklearn.linear_model import LinearRegression
from sklearn.svm import LinearSVC
from sklearn.linear_model import LassoLars
from sklearn.neural_network import MLPRegressor
import matplotlib.mlab as mlab
# Set the randomizer seed so results are the same each time.
np.random.seed(0)
Loading Data
Data Fields
SOC, pH, Ca, P, Sand are the five target variables for predictions. The data have been monotonously transformed from the original measurements and thus include negative values.
PIDN: unique soil sample identifier
SOC: Soil organic carbon
P: Mehlich-3 extractable Phosphorus
Sand: Sand content
m7497.96 - m599.76: There are 3,578 mid-infrared absorbance measurements. For example, the "m7497.96" column is the absorbance at wavenumber 7497.96 cm-1. We suggest you to remove spectra CO2 bands which are in the region m2379.76 to m2352.76, but you do not have to.
Depth: Depth of the soil sample (2 categories: "Topsoil", "Subsoil")
Some potential spatial predictors from remote sensing data sources are also included. Short variable descriptions are provided below and additional descriptions can be found at AfSIS data. The data have been mean centered and scaled.
In [94]:
# Load training data
X = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=range(1, 3594)) # Load columns 1 to 3594 inclusive
n = np.genfromtxt('training.csv',
delimiter=',',
max_rows = 1,
names = True,
usecols=range(1, 3594)) # Load columns 1 to 3594 inclusive
feature_names = np.asarray(n.dtype.names)
Depth = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3594) # Load Depth values
PIDN = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=0) # Load the PIDN for reference
Ca = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3595) # Load Mehlich-3 extractable Calcium data
P = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3596) # Load Mehlich-3 extractable Phosphorus data
pH = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3597) # Load pH data
SOC = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3598) # Load Soil Organic Carbon data
Sand = np.genfromtxt('training.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3599) # Load Sand Content data
# Shuffle the input: create a random permutation of the integers between 0 and the number of data
# points and apply this permutation to X and Y.
# NOTE: Each time you run this cell, you'll re-shuffle the data, resulting in a different ordering.
shuffle = np.random.permutation(np.arange(X.shape[0]))
X, Depth, PIDN, Ca, P, pH, SOC, Sand = X[shuffle], Depth[shuffle], PIDN[shuffle], Ca[shuffle], \
P[shuffle], pH[shuffle], SOC[shuffle], Sand[shuffle]
# Outcome (response) variable list
outcome_vars = ['Ca', 'P', 'pH', 'Soc', 'Sand']
# Color map for outcome variables
colors = ['orange', 'yellowgreen', 'powderblue', 'sienna', 'tan']
In [95]:
# Load test data
test_x = np.genfromtxt('sorted_test.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=range(1, 3594)) # Load columns 0 to 3594 inclusive
test_depth = np.genfromtxt('sorted_test.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=3594) # Load Depth values
test_ids = np.genfromtxt('sorted_test.csv',
delimiter=',',
dtype=None,
skip_header = 1,
usecols=0) # Load the ID column
In [96]:
# Inspect the data shapes
print '\n-------------------------------------------'
print "Training data shape: ", X.shape
print "Feature name shape: ", feature_names.shape
print "PIDN data shape: ", PIDN.shape
print "Depth data shape: ", Depth.shape
print "Ca data shape: ", Ca.shape
print "P data shape: ", P.shape
print "pH data shape: ", pH.shape
print "SOC data shape: ", SOC.shape
print "Sand data shape: ", Sand.shape
print "Test data shape: ", test_x.shape
print "Test_depth data shape: ", test_depth.shape
print "Test_ids data shape: ", test_ids.shape
-------------------------------------------
Training data shape: (1157, 3593)
Feature name shape: (3593,)
PIDN data shape: (1157,)
Depth data shape: (1157,)
Ca data shape: (1157,)
P data shape: (1157,)
pH data shape: (1157,)
SOC data shape: (1157,)
Sand data shape: (1157,)
Test data shape: (727, 3593)
Test_depth data shape: (727,)
Test_ids data shape: (727,)
In [98]:
# Inspect the data in the five response variables
print "Ca: total = %d, max = %0.2f, mean = %0.2f, min = %0.2f" % (Ca.shape[0], np.max(Ca), np.mean(Ca), np.min(Ca))
print "P: total = %d, max = %0.2f, mean = %0.2f, min = %0.2f" % (P.shape[0], np.max(P), np.mean(P), np.min(P))
print "pH: total = %d, max = %0.2f, mean = %0.2f, min = %0.2f" % (pH.shape[0], np.max(pH), np.mean(pH), np.min(pH))
print "SOC: total = %d, max = %0.2f, mean = %0.2f, min = %0.2f" % (SOC.shape[0], np.max(SOC), np.mean(SOC), np.min(SOC))
print "Sand: total = %d, max = %0.2f, mean = %0.2f, min = %0.2f" % (Sand.shape[0], np.max(Sand),
np.mean(Sand), np.min(Sand))
def plot_hist(ind, data, max_y, title, color):
counts, bins, patches = ax[ind].hist(data, facecolor=color, edgecolor='gray')
# set the ticks to be at the edges of the bins.
ax[ind].set_xticks(bins)
# set the limits for x and y
ax[ind].set_xlim([np.min(data),np.max(data)])
ax[ind].set_ylim([0,max_y])
# set the xaxis's tick labels to be formatted with 1 decimal place
ax[ind].xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
ax[ind].set_title(title, fontsize=18)
# Label the raw counts and the percentages below the x-axis
bin_centers = 0.5 * np.diff(bins) + bins[:-1]
for count, x in zip(counts, bin_centers):
# Label the raw counts
ax[ind].annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'),
xytext=(0, -18), textcoords='offset points', va='top', ha='center')
# Label the percentages
percent = '%0.1f%%' % (100 * float(count) / counts.sum())
ax[ind].annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'),
xytext=(0, -32), textcoords='offset points', va='top', ha='center')
fig, ax = plt.subplots(3, 2, figsize=(15, 20))
fig.subplots_adjust(hspace = 0.5, wspace=.2)
ax = ax.ravel()
# Ca
plot_hist(0, Ca, Ca.shape[0], 'Ca Value Histogram', colors[0])
# P
plot_hist(1, P, P.shape[0], 'P Value Histogram', colors[1])
#pH
plot_hist(2, pH, pH.shape[0], 'pH Value Histogram', colors[2])
#SOC
plot_hist(3, SOC, SOC.shape[0], 'SOC Value Histogram', colors[3])
#Sand
plot_hist(4, Sand, Sand.shape[0], 'Sand Value Histogram', colors[4])
# delete the last subplot
fig.delaxes(ax[5])
Ca: total = 1157, max = 9.65, mean = 0.01, min = -0.54
P: total = 1157, max = 13.27, mean = -0.01, min = -0.42
pH: total = 1157, max = 3.42, mean = -0.03, min = -1.89
SOC: total = 1157, max = 7.62, mean = 0.08, min = -0.86
Sand: total = 1157, max = 2.25, mean = -0.01, min = -1.49
In [99]:
# Inspect the data in the predictor variables
def plot_data(ind, data_x, data_y, aspect, title, color):
ax[ind].set_title(title, fontsize=18)
ax[ind].set_xlabel('Predictor Values', fontsize=14)
ax[ind].set_ylabel('Ca Values', fontsize=12)
ax[ind].set_aspect(aspect = aspect, adjustable='box')
ax[ind].grid(True)
ax[ind].scatter(data_x, data_y, color = color, alpha = 0.2, marker = 'o', edgecolors = 'black')
# set up the grid plot
fig, ax = plt.subplots(2, 3, figsize=(15, 20))
#fig.subplots_adjust(hspace = 0.5, wspace=.2)
ax = ax.ravel()
# select the predictor range (note this is influenced by the PCA below)
my_col = 20
X_sub = np.ravel(X[:,:my_col].reshape(-1,1))
# Ca
plot_data(0, X_sub, np.repeat(Ca, my_col), 0.1, 'Ca vs. %d Predictors' % my_col, colors[0])
# P
plot_data(1, X_sub, np.repeat(P, my_col), 0.1, 'P vs. %d Predictors' % my_col, colors[1])
#pH
plot_data(2, X_sub, np.repeat(pH, my_col), 0.2, 'pH vs. %d Predictors' % my_col, colors[2])
#SOC
plot_data(3, X_sub, np.repeat(SOC, my_col), 0.1, 'SOC vs. %d Predictors' % my_col, colors[3])
#Sand
plot_data(4, X_sub, np.repeat(Sand, my_col), 0.35, 'Sand vs. %d Predictors' % my_col, colors[4])
# delete the last subplot
fig.delaxes(ax[5])
Which features have more impact?
There are over three thousand features in this data, with few rows. Thus, we have a large k but small n data set to work with. Perhaps there is a subset of features to focus on.
Below, we investigate two variations of PCA to explain variances over the features. We observe that the first 20 components explain increasing portions of the variance, however after 20 components, the subsequent ones don't really help. The first 70-80 features will explain ~100% of the variance.
In [100]:
# Linear PCA using all of the features
n_comp = feature_names.shape[0]
pca_lin = PCA(n_components = n_comp)
pca_lin.fit(X)
pca_lin_cumsum = np.cumsum(pca_lin.explained_variance_ratio_)
# Non-linear kernel RBF PCA using all of the features
pca_kern = KernelPCA(n_components = n_comp, kernel = 'rbf')
pca_kern.fit(X)
# build the explained variance ratio list for pca_kern
explained_var_ratio_kern = []
for i in range(0, pca_kern.lambdas_.shape[0]):
explained_var_ratio_kern.append(pca_kern.lambdas_[i]/sum(pca_kern.lambdas_))
pca_kern_cumsum = np.cumsum(np.asarray(explained_var_ratio_kern))
# Plot the Information Gain graph
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(pca_lin_cumsum, color = 'purple', marker = 'o', ms = 5, mfc = 'red', label = 'pca_lin')
ax.plot(pca_kern_cumsum, color = 'purple', marker = 'o', ms = 5, mfc = 'yellow', label = 'pca_kern')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.91), shadow=False, scatterpoints=1)
fig.suptitle('Cummulative Information Gain', fontsize=18)
plt.xlabel('Number of Components', fontsize=14)
plt.ylabel('Cummulative Variance Ratio', fontsize=12)
plt.grid(True)
ax.set_xlim([0,30])
ax.set_ylim([0.5,1.0])
# Output variance fractions
print '\n-------------------------------------------'
print 'Fraction of the total variance in the training explained by first k components: \n'
for k in range(1,76):
print("%d \t %s \t %s \t %s" % (k, '{0:.2f}%'.format(pca_lin_cumsum[k-1] * 100),
'{0:.2f}%'.format(pca_kern_cumsum[k-1] * 100), feature_names[k-1]))
-------------------------------------------
Fraction of the total variance in the training explained by first k components:
1 70.54% 66.91% m749796
2 79.38% 75.72% m749604
3 85.54% 82.10% m749411
4 89.31% 85.88% m749218
5 91.69% 88.83% m749025
6 93.63% 90.85% m748832
7 95.14% 92.57% m748639
8 96.16% 94.10% m748446
9 96.75% 95.11% m748254
10 97.27% 95.74% m748061
11 97.74% 96.29% m747868
12 98.10% 96.76% m747675
13 98.42% 97.14% m747482
14 98.69% 97.49% m747289
15 98.90% 97.77% m747097
16 99.06% 98.00% m746904
17 99.20% 98.21% m746711
18 99.34% 98.40% m746518
19 99.44% 98.55% m746325
20 99.50% 98.69% m746132
21 99.56% 98.80% m745939
22 99.62% 98.90% m745747
23 99.67% 98.99% m745554
24 99.72% 99.07% m745361
25 99.75% 99.14% m745168
26 99.78% 99.20% m744975
27 99.81% 99.26% m744782
28 99.83% 99.31% m744589
29 99.85% 99.36% m744397
30 99.86% 99.40% m744204
31 99.88% 99.43% m744011
32 99.89% 99.46% m743818
33 99.90% 99.49% m743625
34 99.91% 99.52% m743432
35 99.92% 99.54% m74324
36 99.93% 99.57% m743047
37 99.93% 99.59% m742854
38 99.94% 99.61% m742661
39 99.94% 99.63% m742468
40 99.95% 99.65% m742275
41 99.95% 99.67% m742082
42 99.96% 99.69% m74189
43 99.96% 99.70% m741697
44 99.96% 99.71% m741504
45 99.96% 99.73% m741311
46 99.97% 99.74% m741118
47 99.97% 99.75% m740925
48 99.97% 99.76% m740733
49 99.97% 99.77% m74054
50 99.98% 99.78% m740347
51 99.98% 99.79% m740154
52 99.98% 99.80% m739961
53 99.98% 99.80% m739768
54 99.98% 99.81% m739575
55 99.98% 99.82% m739383
56 99.98% 99.83% m73919
57 99.99% 99.83% m738997
58 99.99% 99.84% m738804
59 99.99% 99.84% m738611
60 99.99% 99.85% m738418
61 99.99% 99.85% m738225
62 99.99% 99.86% m738033
63 99.99% 99.86% m73784
64 99.99% 99.87% m737647
65 99.99% 99.87% m737454
66 99.99% 99.88% m737261
67 99.99% 99.88% m737068
68 99.99% 99.88% m736876
69 99.99% 99.89% m736683
70 99.99% 99.89% m73649
71 99.99% 99.89% m736297
72 99.99% 99.90% m736104
73 99.99% 99.90% m735911
74 99.99% 99.90% m735718
75 100.00% 99.90% m735526
Feature selectors
In [101]:
# Set up the feature selectors to trial
def getFeatureSelectors():
return [
#['lasso', SelectFromModel(LassoCV()) ], # Doesn't work
#['linearc0.01', SelectFromModel(LinearSVC(C=0.01, penalty="l1")) ],
#['linearc0.1', SelectFromModel(LinearSVC(C=0.1, penalty="l1")) ],
#['linearc11', SelectFromModel(LinearSVC(C=1, penalty="l1")) ],
#['kbest100', SelectKBest(k=100)],
#['kbest250', SelectKBest(k=250)],
#['pca5', PCA(n_components=5)],
#['pca10', PCA(n_components=10)],
['pca20', PCA(n_components=20)],
['pca30', PCA(n_components=30)],
['pca30rbf', KernelPCA(n_components=30,kernel='rbf')],
['pca20kbest5', FeatureUnion([("pca5", PCA(n_components=20)), ("kbest5", SelectKBest(k=5))])],
['pca20kbest50', FeatureUnion([("pca5", PCA(n_components=20)), ("kbest50", SelectKBest(k=50))])],
['pca20kbest250', FeatureUnion([("pca5", PCA(n_components=20)), ("kbest250", SelectKBest(k=250))])],
]
Classifiers
In [103]:
# Prep data for modeling
train_data, dev_data, Depth_train, Depth_dev, Ca_train, Ca_dev, P_train, P_dev, pH_train, pH_dev, \
SOC_train, SOC_dev, Sand_train, Sand_dev = train_test_split(X, Depth, Ca, P, pH, SOC, Sand)
train_labels = [Ca_train, P_train, pH_train, SOC_train, Sand_train]
dev_labels = [Ca_dev, P_dev, pH_dev, SOC_dev, Sand_dev]
# Inspect the data shapes
print '\n-------------------------------------------'
print "train data shape: ", train_data.shape
print "dev data shape: ", dev_data.shape
print "Depth_train data shape: ", Depth_train.shape
print "Depth_dev data shape: ", Depth_dev.shape
print "Ca_train data shape: ", Ca_train.shape
print "Ca_dev data shape: ", Ca_dev.shape
print "P_train data shape: ", P_train.shape
print "P_dev data shape: ", P_dev.shape
print "pH_train data shape: ", pH_train.shape
print "pH_dev data shape: ", pH_dev.shape
print "SOC_train data shape: ", SOC_train.shape
print "SOC_dev data shape: ", SOC_dev.shape
print "Sand_train data shape: ", Sand_train.shape
print "Sand_dev data shape: ", Sand_dev.shape
-------------------------------------------
train data shape: (867, 3593)
dev data shape: (290, 3593)
Depth_train data shape: (867,)
Depth_dev data shape: (290,)
Ca_train data shape: (867,)
Ca_dev data shape: (290,)
P_train data shape: (867,)
P_dev data shape: (290,)
pH_train data shape: (867,)
pH_dev data shape: (290,)
SOC_train data shape: (867,)
SOC_dev data shape: (290,)
Sand_train data shape: (867,)
Sand_dev data shape: (290,)
In [104]:
# Predict the mean value of an array
def PredictMean(labels):
mean = np.mean(labels)
mean_array = mean*np.ones(labels.shape)
return mean_array
# Get the means of the dev data for our reference
print '\n-------------------------------------------'
Ca_mean = PredictMean(Ca_dev)
print 'Calcium Mean: ', Ca_mean[0]
P_mean = PredictMean(P_dev)
print 'Phosphorus Mean: ', P_mean[0]
pH_mean = PredictMean(pH_dev)
print 'pH Mean: ', pH_mean[0]
SOC_mean = PredictMean(SOC_dev)
print 'SOC Mean: ', SOC_mean[0]
Sand_mean = PredictMean(Sand_dev)
print 'Sand Mean: ', Sand_mean[0]
-------------------------------------------
Calcium Mean: 0.0438257090646
Phosphorus Mean: 0.00645008076791
pH Mean: 0.025710394719
SOC Mean: 0.0286130670532
Sand Mean: 0.0256980369452
In [105]:
# Set up the classifiers to trial
def getClassifiers():
return [
['KNN', KNeighborsRegressor(), {'n_neighbors':[1, 2, 3, 5, 8]}],
['SVRdict', SVR(cache_size=200), {'C':[0.1,1.0,100.0,1000.0],'kernel':['linear','rbf']}],
#['SVR', SVR(cache_size=200, kernel='linear', C=1.0, epsilon=0.05, shrinking=False),{}]
['Lasso', Lasso(), {'alpha':[0.01, 0.05, 0.25, 0.9]}],
#['LassoLars', LassoLars(), {'alpha':[0.01, 0.1, 0.5, 1.0]}],
['RandomForest', RandomForestRegressor(), {'n_estimators':[1, 2, 3, 5, 8]}],
['nn', MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(2, 2)), {}]
#['RandomForest', RandomForestRegressor(), {'n_estimators':[1, 2, 3, 5, 8]}]
]
Test combinations of selectors and classifiers
In [82]:
# For each outcome variable, for each classifier and for each selector, it will obtain and print
# the best hyperparameters and inmediately print the mean squared error.
#
# Finally, after finishing the calculations, it will print the methods and score ordered by score (MSE)
# To see what would be the final score we'd get, you can get the best MSE score for each outcome variable,
# and calculate the average of them.
def run():
allResults = []
scaler = Normalizer().fit(train_data)
transformedTrainData = scaler.transform(train_data)
transformedDevData = scaler.transform(dev_data)
for outcomeVarIndex in range(0, 5):
print('*************************************************************')
print('Outcome Variable:', outcome_vars[outcomeVarIndex])
print('*************************************************************')
results = []
scaler = Normalizer().fit(train_data)
# Get the mean value of the outcome variable
DevMean = PredictMean(dev_labels[outcomeVarIndex])[0]
for selector in getFeatureSelectors():
selectedTrainData = selector[1].fit(transformedTrainData,
train_labels[outcomeVarIndex]).transform(transformedTrainData)
selectedDevData = selector[1].transform(transformedDevData)
for classifier in getClassifiers():
print('-------------------------------------------------------')
print(selector[0] + ' ' + classifier[0])
grid_search = GridSearchCV(classifier[1], param_grid=classifier[2],cv=5)
grid_search.fit(selectedTrainData, train_labels[outcomeVarIndex])
print(grid_search.best_estimator_)
# Mean Squared Error: (y_true - y_pred)**2.sum()
meanSquaredError = 0.0
for i in range(len(selectedDevData)):
diff = grid_search.predict(selectedDevData[i].reshape(1, -1)) - dev_labels[outcomeVarIndex][i]
squaredDiff = diff ** 2
meanSquaredError = meanSquaredError + squaredDiff
#meanSquaredError = meanSquaredError / float(len(selectedDevData))
# Residual Sum of squares: (y_true - y_mean)**2.sum()
residualSquaredError = 0.0
for i in range(len(selectedDevData)):
diff = dev_labels[outcomeVarIndex][i] - DevMean
squaredDiff = diff ** 2
residualSquaredError = residualSquaredError + squaredDiff
myScore = 1 - meanSquaredError/residualSquaredError
print('Mean Squared Error: ', str(meanSquaredError / float(len(selectedDevData))))
print('Residual Squared Error: ', str(residualSquaredError))
print('Calculated Score: ', str(1 - meanSquaredError/residualSquaredError))
print('Score: ' + str(grid_search.score(selectedDevData, dev_labels[outcomeVarIndex])))
# Store in an array, for each combination, the following:
# [selector name, classifier name, mean squared error, selector instance, classifier instance]
results.append([selector[0], classifier[0], myScore, selector[1], grid_search])
sortedResults = sorted(results, key=lambda result: result[2], reverse=True)
for result in sortedResults:
print('Selector: ' + str(result[0]) + ', Classifier: ' + str(result[1]) + ', Score: ' + str(result[2]))
copyResults = sortedResults[:]
allResults.append(copyResults)
#TODO Calculate columnwise mean of the mean squared error
#Each item has the best result on item 0
squaredErrorSum = 0.0
bestModels = []
print('-------------------------------------------------------')
print('Best Results')
print('-------------------------------------------------------')
for i in range(len(allResults)):
columnResults = allResults[i]
bestColumnResult = columnResults[0]
squaredError = bestColumnResult[2]
print('Outcome Variable: ' + outcome_vars[i] + ', Selector: ' + str(bestColumnResult[0]) +
', Classifier: ' + str(bestColumnResult[1]) + ', Score: ' + str(bestColumnResult[2]))
squaredErrorSum = squaredErrorSum + squaredError
bestModels.append(bestColumnResult)
print('Best result obtained: ' + str(squaredErrorSum / 5.0))
return bestModels
models = run()
*************************************************************
('Outcome Variable:', 'Ca')
*************************************************************
-------------------------------------------------------
pca70 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.07981822]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.90921544]')
Score: 0.909215443939
-------------------------------------------------------
pca70 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.07003102]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.92034732]')
Score: 0.920347321917
-------------------------------------------------------
pca70 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.6786555]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.2281031]')
Score: 0.22810309732
-------------------------------------------------------
pca70 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=5, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.16165428]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.81613582]')
Score: 0.816135819251
-------------------------------------------------------
pca70 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.88193917]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[-0.00310999]')
Score: -0.00310998711999
-------------------------------------------------------
pca80 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.07981822]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.90921544]')
Score: 0.909215443939
-------------------------------------------------------
pca80 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.07063322]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.91966238]')
Score: 0.919662382972
-------------------------------------------------------
pca80 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.6786555]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.2281031]')
Score: 0.22810309732
-------------------------------------------------------
pca80 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=3, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.16131517]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.81652152]')
Score: 0.816521517717
-------------------------------------------------------
pca80 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.88193917]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[-0.00310999]')
Score: -0.00310998713165
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.07981822]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.90921544]')
Score: 0.909215443939
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.69321101]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.21154779]')
Score: 0.211547789117
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.88193917]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[-0.00310999]')
Score: -0.00310998711999
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.1787169]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.79672894]')
Score: 0.796728943928
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.88193921]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[-0.00311003]')
Score: -0.0031100345287
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.07953554]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.90953696]')
Score: 0.909536964037
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.07069246]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.919595]')
Score: 0.919595003377
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.6786555]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.2281031]')
Score: 0.22810309732
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.19174324]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.78191289]')
Score: 0.78191289471
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.10987139]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.87503323]')
Score: 0.875033231205
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.07999062]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.90901936]')
Score: 0.909019358016
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.07086452]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.91939931]')
Score: 0.919399306196
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.6786555]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.2281031]')
Score: 0.22810309732
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=5, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.11977766]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.86376592]')
Score: 0.863765919832
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.88196915]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[-0.00314408]')
Score: -0.00314408368184
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.08992857]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.89771603]')
Score: 0.897716025439
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.06939001]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.92107641]')
Score: 0.92107640674
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.6786555]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.2281031]')
Score: 0.22810309732
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=2, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.20334763]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.76871417]')
Score: 0.768714167829
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.14692797]')
('Residual Squared Error: ', '254.969407087')
('Calculated Score: ', '[ 0.8328854]')
Score: 0.832885396846
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.92107641]
Selector: pca70, Classifier: SVRdict, Score: [ 0.92034732]
Selector: pca80, Classifier: SVRdict, Score: [ 0.91966238]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.919595]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.91939931]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.90953696]
Selector: pca70, Classifier: KNN, Score: [ 0.90921544]
Selector: pca80, Classifier: KNN, Score: [ 0.90921544]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.90921544]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.90901936]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.89771603]
Selector: pca20kbest5, Classifier: nn, Score: [ 0.87503323]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.86376592]
Selector: pca20kbest250, Classifier: nn, Score: [ 0.8328854]
Selector: pca80, Classifier: RandomForest, Score: [ 0.81652152]
Selector: pca70, Classifier: RandomForest, Score: [ 0.81613582]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.79672894]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.78191289]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.76871417]
Selector: pca80, Classifier: Lasso, Score: [ 0.2281031]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.2281031]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.2281031]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.2281031]
Selector: pca70, Classifier: Lasso, Score: [ 0.2281031]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.21154779]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.00310999]
Selector: pca70, Classifier: nn, Score: [-0.00310999]
Selector: pca80, Classifier: nn, Score: [-0.00310999]
Selector: pca30rbf, Classifier: nn, Score: [-0.00311003]
Selector: pca20kbest50, Classifier: nn, Score: [-0.00314408]
*************************************************************
('Outcome Variable:', 'P')
*************************************************************
-------------------------------------------------------
pca70 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.91593735]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03570509]')
Score: 0.035705086761
-------------------------------------------------------
pca70 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.92137248]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.02998301]')
Score: 0.0299830119785
-------------------------------------------------------
pca70 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94324069]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.00696025]')
Score: 0.00696025040975
-------------------------------------------------------
pca70 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.93361414]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.01709505]')
Score: 0.0170950460484
-------------------------------------------------------
pca70 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.94985208]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ -1.89885086e-07]')
Score: -1.89885085211e-07
-------------------------------------------------------
pca80 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.91593735]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03570509]')
Score: 0.035705086761
-------------------------------------------------------
pca80 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.91724218]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03433137]')
Score: 0.0343313672455
-------------------------------------------------------
pca80 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94324069]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.00696025]')
Score: 0.00696025040975
-------------------------------------------------------
pca80 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=2, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 1.72961304]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[-0.82092918]')
Score: -0.820929179163
-------------------------------------------------------
pca80 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.94985208]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ -1.89885064e-07]')
Score: -1.89885064117e-07
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.91593735]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03570509]')
Score: 0.035705086761
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.97600583]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[-0.02753475]')
Score: -0.0275347456746
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94985208]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ -1.89885085e-07]')
Score: -1.89885085211e-07
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 1.00790299]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[-0.06111595]')
Score: -0.0611159450312
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.94985208]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ -1.89885085e-07]')
Score: -1.89885085433e-07
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.91593735]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03570509]')
Score: 0.035705086761
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.9171942]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03438188]')
Score: 0.0343818831082
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94324069]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.00696025]')
Score: 0.00696025040975
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=3, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 1.46437547]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[-0.54168821]')
Score: -0.541688209019
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.88890815]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.06416132]')
Score: 0.0641613189975
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.91746565]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.0340961]')
Score: 0.0340961013044
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.9172952]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03427556]')
Score: 0.0342755567557
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94324069]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.00696025]')
Score: 0.00696025040975
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.97241517]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[-0.02375451]')
Score: -0.023754513446
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.94985208]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ -1.89885085e-07]')
Score: -1.89885085433e-07
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.89494309]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.05780776]')
Score: 0.0578077566802
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.91697261]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.03461518]')
Score: 0.0346151782428
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.94324069]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.00696025]')
Score: 0.00696025040975
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.87330651]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.08058665]')
Score: 0.0805866518784
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.92589826]')
('Residual Squared Error: ', '275.457050536')
('Calculated Score: ', '[ 0.02521829]')
Score: 0.0252182891215
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.08058665]
Selector: pca20kbest5, Classifier: nn, Score: [ 0.06416132]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.05780776]
Selector: pca70, Classifier: KNN, Score: [ 0.03570509]
Selector: pca80, Classifier: KNN, Score: [ 0.03570509]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.03570509]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.03570509]
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.03461518]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.03438188]
Selector: pca80, Classifier: SVRdict, Score: [ 0.03433137]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.03427556]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.0340961]
Selector: pca70, Classifier: SVRdict, Score: [ 0.02998301]
Selector: pca20kbest250, Classifier: nn, Score: [ 0.02521829]
Selector: pca70, Classifier: RandomForest, Score: [ 0.01709505]
Selector: pca70, Classifier: Lasso, Score: [ 0.00696025]
Selector: pca80, Classifier: Lasso, Score: [ 0.00696025]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.00696025]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.00696025]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.00696025]
Selector: pca80, Classifier: nn, Score: [ -1.89885064e-07]
Selector: pca30rbf, Classifier: Lasso, Score: [ -1.89885085e-07]
Selector: pca20kbest50, Classifier: nn, Score: [ -1.89885085e-07]
Selector: pca30rbf, Classifier: nn, Score: [ -1.89885085e-07]
Selector: pca70, Classifier: nn, Score: [ -1.89885086e-07]
Selector: pca20kbest50, Classifier: RandomForest, Score: [-0.02375451]
Selector: pca30rbf, Classifier: SVRdict, Score: [-0.02753475]
Selector: pca30rbf, Classifier: RandomForest, Score: [-0.06111595]
Selector: pca20kbest5, Classifier: RandomForest, Score: [-0.54168821]
Selector: pca80, Classifier: RandomForest, Score: [-0.82092918]
*************************************************************
('Outcome Variable:', 'pH')
*************************************************************
-------------------------------------------------------
pca70 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.34201272]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.59736656]')
Score: 0.597366560654
-------------------------------------------------------
pca70 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.16893322]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.80112388]')
Score: 0.801123876514
-------------------------------------------------------
pca70 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.668395]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.21313401]')
Score: 0.213134010547
-------------------------------------------------------
pca70 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.3076552]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.63781385]')
Score: 0.637813852417
-------------------------------------------------------
pca70 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.85330837]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[-0.00455469]')
Score: -0.00455469030031
-------------------------------------------------------
pca80 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.34201272]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.59736656]')
Score: 0.597366560654
-------------------------------------------------------
pca80 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.16695764]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.80344963]')
Score: 0.803449627555
-------------------------------------------------------
pca80 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.668395]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.21313401]')
Score: 0.213134010547
-------------------------------------------------------
pca80 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.28971982]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.65892822]')
Score: 0.658928219611
-------------------------------------------------------
pca80 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.23728028]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.72066251]')
Score: 0.720662507329
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.34201272]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.59736656]')
Score: 0.597366560654
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.59770088]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.29635845]')
Score: 0.296358451417
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.85330837]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[-0.00455469]')
Score: -0.00455469030032
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.29249307]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.65566342]')
Score: 0.655663415592
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.85330974]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[-0.0045563]')
Score: -0.00455629890162
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.34201272]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.59736656]')
Score: 0.597366560654
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.16674204]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.80370344]')
Score: 0.803703439724
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.668395]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.21313401]')
Score: 0.213134010547
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=5, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.33023255]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.61123474]')
Score: 0.611234735398
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.85330837]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[-0.00455469]')
Score: -0.0045546903075
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.31600562]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.62798334]')
Score: 0.627983343695
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.16592667]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.80466333]')
Score: 0.804663334574
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.668395]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.21313401]')
Score: 0.213134010547
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.24136629]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.71585227]')
Score: 0.715852272945
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.40670197]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.52121134]')
Score: 0.521211337634
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.32031832]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.62290623]')
Score: 0.622906234043
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.16659369]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.80387808]')
Score: 0.803878083683
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.668395]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.21313401]')
Score: 0.213134010547
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.25797573]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[ 0.69629885]')
Score: 0.696298853933
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.85330897]')
('Residual Squared Error: ', '246.337436654')
('Calculated Score: ', '[-0.00455539]')
Score: -0.00455539492479
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.80466333]
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.80387808]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.80370344]
Selector: pca80, Classifier: SVRdict, Score: [ 0.80344963]
Selector: pca70, Classifier: SVRdict, Score: [ 0.80112388]
Selector: pca80, Classifier: nn, Score: [ 0.72066251]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.71585227]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.69629885]
Selector: pca80, Classifier: RandomForest, Score: [ 0.65892822]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.65566342]
Selector: pca70, Classifier: RandomForest, Score: [ 0.63781385]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.62798334]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.62290623]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.61123474]
Selector: pca70, Classifier: KNN, Score: [ 0.59736656]
Selector: pca80, Classifier: KNN, Score: [ 0.59736656]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.59736656]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.59736656]
Selector: pca20kbest50, Classifier: nn, Score: [ 0.52121134]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.29635845]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.21313401]
Selector: pca70, Classifier: Lasso, Score: [ 0.21313401]
Selector: pca80, Classifier: Lasso, Score: [ 0.21313401]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.21313401]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.21313401]
Selector: pca70, Classifier: nn, Score: [-0.00455469]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.00455469]
Selector: pca20kbest5, Classifier: nn, Score: [-0.00455469]
Selector: pca20kbest250, Classifier: nn, Score: [-0.00455539]
Selector: pca30rbf, Classifier: nn, Score: [-0.0045563]
*************************************************************
('Outcome Variable:', 'Soc')
*************************************************************
-------------------------------------------------------
pca70 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.26590921]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.76594117]')
Score: 0.765941168626
-------------------------------------------------------
pca70 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.18219312]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.83962982]')
Score: 0.839629815543
-------------------------------------------------------
pca70 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.62146762]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.45297124]')
Score: 0.452971243065
-------------------------------------------------------
pca70 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.28482702]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.74928932]')
Score: 0.749289316167
-------------------------------------------------------
pca70 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.28596924]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.74828391]')
Score: 0.74828391125
-------------------------------------------------------
pca80 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.26590921]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.76594117]')
Score: 0.765941168626
-------------------------------------------------------
pca80 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.1783629]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.84300126]')
Score: 0.843001260737
-------------------------------------------------------
pca80 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.62146762]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.45297124]')
Score: 0.452971243065
-------------------------------------------------------
pca80 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.32748545]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.71174048]')
Score: 0.711740479276
-------------------------------------------------------
pca80 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.13954524]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[-0.00305147]')
Score: -0.00305147356543
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.26590921]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.76594117]')
Score: 0.765941168626
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.72871071]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.35857364]')
Score: 0.358573644598
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 1.13954541]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[-0.00305163]')
Score: -0.00305162643852
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.23536375]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.79282792]')
Score: 0.792827918284
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.13954584]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[-0.003052]')
Score: -0.00305200221138
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.23449298]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.79359439]')
Score: 0.793594387363
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.17844565]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.84292842]')
Score: 0.842928416632
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.62146762]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.45297124]')
Score: 0.452971243065
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.29095921]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.74389164]')
Score: 0.743891637584
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.13954534]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[-0.00305157]')
Score: -0.00305156503712
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=2, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.2344193]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.79365925]')
Score: 0.793659246916
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.17591771]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.84515356]')
Score: 0.84515356353
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.62146762]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.45297124]')
Score: 0.452971243065
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.19947003]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.82442232]')
Score: 0.824422318137
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.12573744]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.88932328]')
Score: 0.889323284157
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.23571117]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.79252211]')
Score: 0.792522114823
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.17444844]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.84644684]')
Score: 0.846446843057
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.62146762]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.45297124]')
Score: 0.452971243065
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=5, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.21483655]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[ 0.81089639]')
Score: 0.810896392095
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 1.13954541]')
('Residual Squared Error: ', '329.462772286')
('Calculated Score: ', '[-0.00305163]')
Score: -0.00305162790114
Selector: pca20kbest50, Classifier: nn, Score: [ 0.88932328]
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.84644684]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.84515356]
Selector: pca80, Classifier: SVRdict, Score: [ 0.84300126]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.84292842]
Selector: pca70, Classifier: SVRdict, Score: [ 0.83962982]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.82442232]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.81089639]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.79365925]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.79359439]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.79282792]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.79252211]
Selector: pca70, Classifier: KNN, Score: [ 0.76594117]
Selector: pca80, Classifier: KNN, Score: [ 0.76594117]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.76594117]
Selector: pca70, Classifier: RandomForest, Score: [ 0.74928932]
Selector: pca70, Classifier: nn, Score: [ 0.74828391]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.74389164]
Selector: pca80, Classifier: RandomForest, Score: [ 0.71174048]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.45297124]
Selector: pca70, Classifier: Lasso, Score: [ 0.45297124]
Selector: pca80, Classifier: Lasso, Score: [ 0.45297124]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.45297124]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.45297124]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.35857364]
Selector: pca80, Classifier: nn, Score: [-0.00305147]
Selector: pca20kbest5, Classifier: nn, Score: [-0.00305157]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.00305163]
Selector: pca20kbest250, Classifier: nn, Score: [-0.00305163]
Selector: pca30rbf, Classifier: nn, Score: [-0.003052]
*************************************************************
('Outcome Variable:', 'Sand')
*************************************************************
-------------------------------------------------------
pca70 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.18596459]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.80828733]')
Score: 0.808287331031
-------------------------------------------------------
pca70 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.16229178]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.83269185]')
Score: 0.83269185159
-------------------------------------------------------
pca70 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43373292]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.55286059]')
Score: 0.55286059093
-------------------------------------------------------
pca70 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.22866957]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.76426236]')
Score: 0.764262357586
-------------------------------------------------------
pca70 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.98221937]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[-0.01257932]')
Score: -0.0125793223439
-------------------------------------------------------
pca80 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.18596459]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.80828733]')
Score: 0.808287331031
-------------------------------------------------------
pca80 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.15940433]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.83566856]')
Score: 0.835668558137
-------------------------------------------------------
pca80 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43373292]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.55286059]')
Score: 0.55286059093
-------------------------------------------------------
pca80 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.23673177]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.75595096]')
Score: 0.755950958064
-------------------------------------------------------
pca80 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.33515792]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.6544825]')
Score: 0.654482500897
-------------------------------------------------------
pca30rbf KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.18596459]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.80828733]')
Score: 0.808287331031
-------------------------------------------------------
pca30rbf SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.36960216]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.6189736]')
Score: 0.618973604782
-------------------------------------------------------
pca30rbf Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.98223286]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[-0.01259324]')
Score: -0.0125932370377
-------------------------------------------------------
pca30rbf RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.22165091]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.77149796]')
Score: 0.771497963167
-------------------------------------------------------
pca30rbf nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.3464114]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.64288118]')
Score: 0.642881180469
-------------------------------------------------------
pca20kbest5 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.1866358]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.80759537]')
Score: 0.807595369827
-------------------------------------------------------
pca20kbest5 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.15924459]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.83583323]')
Score: 0.835833233144
-------------------------------------------------------
pca20kbest5 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43373292]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.55286059]')
Score: 0.55286059093
-------------------------------------------------------
pca20kbest5 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.22238237]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.7707439]')
Score: 0.770743896934
-------------------------------------------------------
pca20kbest5 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.16304882]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.83191142]')
Score: 0.831911419503
-------------------------------------------------------
pca20kbest50 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=3, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.19514192]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.79882633]')
Score: 0.798826330243
-------------------------------------------------------
pca20kbest50 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.15977876]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.83528255]')
Score: 0.835282553719
-------------------------------------------------------
pca20kbest50 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43373292]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.55286059]')
Score: 0.55286059093
-------------------------------------------------------
pca20kbest50 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.22055788]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.77262477]')
Score: 0.772624771677
-------------------------------------------------------
pca20kbest50 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.14493471]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.85058542]')
Score: 0.850585423875
-------------------------------------------------------
pca20kbest250 KNN
KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=5, p=2,
weights='uniform')
('Mean Squared Error: ', '[ 0.17128044]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.82342536]')
Score: 0.823425360717
-------------------------------------------------------
pca20kbest250 SVRdict
SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',
kernel='linear', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
('Mean Squared Error: ', '[ 0.15807937]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.83703447]')
Score: 0.837034467669
-------------------------------------------------------
pca20kbest250 Lasso
Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,
normalize=False, positive=False, precompute=False, random_state=None,
selection='cyclic', tol=0.0001, warm_start=False)
('Mean Squared Error: ', '[ 0.43373292]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.55286059]')
Score: 0.55286059093
-------------------------------------------------------
pca20kbest250 RandomForest
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_impurity_split=1e-07, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=8, n_jobs=1, oob_score=False, random_state=None,
verbose=0, warm_start=False)
('Mean Squared Error: ', '[ 0.19826354]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[ 0.79560823]')
Score: 0.795608226336
-------------------------------------------------------
pca20kbest250 nn
MLPRegressor(activation='relu', alpha=1e-05, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=(2, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=200, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=None,
shuffle=True, solver='lbfgs', tol=0.0001, validation_fraction=0.1,
verbose=False, warm_start=False)
('Mean Squared Error: ', '[ 0.98224942]')
('Residual Squared Error: ', '281.304990422')
('Calculated Score: ', '[-0.0126103]')
Score: -0.0126102986887
Selector: pca20kbest50, Classifier: nn, Score: [ 0.85058542]
Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.83703447]
Selector: pca20kbest5, Classifier: SVRdict, Score: [ 0.83583323]
Selector: pca80, Classifier: SVRdict, Score: [ 0.83566856]
Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.83528255]
Selector: pca70, Classifier: SVRdict, Score: [ 0.83269185]
Selector: pca20kbest5, Classifier: nn, Score: [ 0.83191142]
Selector: pca20kbest250, Classifier: KNN, Score: [ 0.82342536]
Selector: pca70, Classifier: KNN, Score: [ 0.80828733]
Selector: pca80, Classifier: KNN, Score: [ 0.80828733]
Selector: pca30rbf, Classifier: KNN, Score: [ 0.80828733]
Selector: pca20kbest5, Classifier: KNN, Score: [ 0.80759537]
Selector: pca20kbest50, Classifier: KNN, Score: [ 0.79882633]
Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.79560823]
Selector: pca20kbest50, Classifier: RandomForest, Score: [ 0.77262477]
Selector: pca30rbf, Classifier: RandomForest, Score: [ 0.77149796]
Selector: pca20kbest5, Classifier: RandomForest, Score: [ 0.7707439]
Selector: pca70, Classifier: RandomForest, Score: [ 0.76426236]
Selector: pca80, Classifier: RandomForest, Score: [ 0.75595096]
Selector: pca80, Classifier: nn, Score: [ 0.6544825]
Selector: pca30rbf, Classifier: nn, Score: [ 0.64288118]
Selector: pca30rbf, Classifier: SVRdict, Score: [ 0.6189736]
Selector: pca70, Classifier: Lasso, Score: [ 0.55286059]
Selector: pca20kbest50, Classifier: Lasso, Score: [ 0.55286059]
Selector: pca80, Classifier: Lasso, Score: [ 0.55286059]
Selector: pca20kbest250, Classifier: Lasso, Score: [ 0.55286059]
Selector: pca20kbest5, Classifier: Lasso, Score: [ 0.55286059]
Selector: pca70, Classifier: nn, Score: [-0.01257932]
Selector: pca30rbf, Classifier: Lasso, Score: [-0.01259324]
Selector: pca20kbest250, Classifier: nn, Score: [-0.0126103]
-------------------------------------------------------
Best Results
-------------------------------------------------------
Outcome Variable: Ca, Selector: pca20kbest250, Classifier: SVRdict, Score: [ 0.92107641]
Outcome Variable: P, Selector: pca20kbest250, Classifier: RandomForest, Score: [ 0.08058665]
Outcome Variable: pH, Selector: pca20kbest50, Classifier: SVRdict, Score: [ 0.80466333]
Outcome Variable: Soc, Selector: pca20kbest50, Classifier: nn, Score: [ 0.88932328]
Outcome Variable: Sand, Selector: pca20kbest50, Classifier: nn, Score: [ 0.85058542]
Best result obtained: [ 0.70924702]
Predictions based on our dev data
Test on the evaluation data
In [90]:
myScore = []
scaler = Normalizer().fit(train_data)
transformedDevData = scaler.transform(dev_data)
#transformedEvalData = eval_data
# Use the appropriate model to estimate the 5 outcome variables
for outcomeVarIndex in range(0, 5):
# Grab selector and classifier
selector = models[outcomeVarIndex][3]
classifier = models[outcomeVarIndex][4]
# Transform the input variables
selectedSample = selector.transform(transformedDevData)
# Predict
myScore.append(classifier.score(selectedSample,dev_labels[outcomeVarIndex]))
print(myScore)
[0.92107640674007973, 0.080586651878408744, 0.80466333457428796, 0.88932328415726192, 0.85058542387502534]
In [91]:
scaler = Normalizer().fit(X)
normalizedTestData = scaler.transform(test_x)
#normalizedTestData = test_x
allPredictions = []
# Iterate through test samples
for sampleIndex in range(len(test_x)):
sampleId = test_ids[sampleIndex]
sample = normalizedTestData[sampleIndex]
currentSamplePredictions = []
# Use the appropriate model to estimate the 5 outcome variables
for outcomeVarIndex in range(0, 5):
# Grab selector and classifier
selector = models[outcomeVarIndex][3]
classifier = models[outcomeVarIndex][4]
# Transform the input variables
selectedSample = selector.transform(sample.reshape(1, -1))
# Predict
predicted = classifier.predict(selectedSample.reshape(1, -1))
# Store
currentSamplePredictions.append(predicted[0])
allPredictions.append(currentSamplePredictions)
print(allPredictions)
[[-0.58403570657743575, -0.1909150301909433, -0.84903611740725582, -0.52404121657626757, 1.1336125196767906], [0.60115692775251528, 0.88639711207728689, 1.1974971266651582, -0.41766977548718165, -1.0506234617267143], [-0.10812788364172565, 0.29759328472201824, 0.084227254535089724, -0.1342391951169723, -0.76410674151392977], [-0.24469367868571812, -0.32017519493339963, -0.45537876771951558, 0.74085814844123332, -1.0506234617267143], [-0.46212871466516692, 0.16876542822284751, -1.5630781863725045, 0.45687142669637149, -0.10523414800724362], [-0.6678993524817658, 0.20335008768571255, -0.71116998676150134, -0.52404121657626757, 0.3045401644374004], [-0.41955312479470042, 0.62095985069980175, -1.0618555326368955, 0.72758163678055077, -1.0506234617267143], [-0.23386406434384543, 2.1837541501779909, 0.76600373942487066, -0.52404121657626757, 0.45645706431931066], [0.066794468988365274, -0.14465804815936212, 0.60401912181696593, -0.13553783518817625, -1.0506234617267143], [-0.7088997897589282, 1.3701500363141053, -1.4514740539318578, -0.51735323480202, 0.17281840457142983], [-0.56316850414321795, 0.76189233801097433, -0.57463768048019803, 0.45189177799600189, -0.20235490202954731], [-0.52255886196607282, 0.85138014437113685, -0.63832815303938562, -0.52404121657626757, 1.5343980291765609], [0.61451276597502424, -0.037877912067768124, 1.3486495175893807, -0.52404121657626757, -0.050649713789512885], [-0.3608982847181581, 0.15104079024813, -1.1031772409330967, -0.11774295450834377, -0.92585430612370856], [-0.45810601523797079, -0.19048272194765764, -0.021181251170400817, -0.52404121657626757, 0.27266125021088139], [-0.22459584769431462, 0.59415673961608162, -0.42184347569416447, -0.52404121657626757, 0.22978786295705755], [-0.30215601752024401, -0.16757038505351002, -0.3483654240073375, -0.52404121657626757, 0.70073755697914741], [-0.30275229109265656, -0.11915186180549955, 0.21264264002759203, -0.52404121657626757, 0.64024095395883007], [0.82194180823991569, -0.11137031342635517, 1.4868344457028333, 0.65660219079919591, -0.55112414892658501], [-0.39235463701647877, 1.5339948605194267, 0.15110027055517561, -0.52404121657626757, 0.87598131964066428], [-0.64204660797328206, -0.043930227473769248, -0.34399744361793738, -0.52404121657626757, 1.7619300492430188], [-0.34128614317245098, -0.29769516628253773, -0.90356892960736745, 0.6339153117540175, -0.55633501646958061], [0.43169581789736622, 0.54227975042178433, 1.5763129253767292, -0.52404121657626757, -0.41999692232713881], [-0.20218341753944191, -0.20474889397608911, 0.20543545936910901, 0.0053385591826704548, -0.96220803788710962], [-0.42148430986143826, -0.0097778762541907305, -0.48924769266939139, -0.52404121657626757, -0.13073913838080997], [-0.35206464836971652, -0.083270277612777679, 0.19414770086551014, -0.52404121657626757, 1.257489290061143], [0.72098912358478895, 0.11861767200169364, 1.7764191010889934, -0.52404121657626757, -0.39803201568780111], [-0.32879124336549825, -0.12866264315778739, 0.19641458673802559, -0.52404121657626757, 0.86326795355997699], [0.33572536440231016, 0.61015214461765654, 1.4499598553720818, -0.52404121657626757, 0.19846889055423933], [-0.53540416526255985, -0.062951790178344597, -0.29617616968380434, -0.52404121657626757, 0.96939615982162386], [-0.35345788250418497, 1.5668502870091481, 0.22641647745212579, -0.52404121657626757, 0.85015852321489538], [0.10665462705915996, 0.019186776045958365, 0.011839699829330197, 0.068686755302363123, -0.91385246335508241], [-0.4917146275543276, -0.010642492740762139, -1.4632070398850101, 0.4321779333651774, 0.55731985004710372], [-0.54827578172365765, -0.29337208384967961, -0.26795109343505574, -0.52404121657626757, -0.51224809074507072], [-0.45603118867880443, 2.1915356985571348, -1.3372479379352904, 0.83492457054078062, -1.0506234617267143], [-0.57827529026613167, 1.0264649829018877, -1.5668602177737516, 3.3797399634007546, 0.74414198579959634], 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Generate CSV file for kaggle submission
In [92]:
# Print header
header = 'PIDN,Ca,P,pH,SOC,Sand'
#np.savetxt('test.out', header, delimiter=',')
filename = 'test7v2.csv'
# Clean file
open(filename, 'w').close()
with open(filename, 'w') as f:
f.write('PIDN,Ca,P,pH,SOC,Sand\n') # python will convert \n to os.linesep
# Iterate through test samples
for i in range(len(allPredictions)):
pred = allPredictions[i]
testId = test_ids[i]
text = testId + ',' + str(pred[0]) + ',' + str(pred[1]) + ',' + str(pred[2]) + ',' + str(pred[3]) + ',' + str(pred[4]) + '\n'
f.write(text)
f.close()
Investigation: Sand variable
In [ ]:
#!/usr/bin/env python
# the histogram of the data
n, bins, patches = plt.hist(train_Sand_labels, 50, normed=1, facecolor='green', alpha=0.75)
plt.title(r'$\mathrm{Sand:}$')
#plt.axis([40, 160, 0, 0.03])
plt.grid(True)
plt.show()
scaler = Normalizer().fit(train_data)
transformedTrainData = scaler.transform(train_data)
transformedDevData = scaler.transform(dev_data)
selector = FeatureUnion([("pca5", PCA(n_components=20)), ("kbest5", SelectKBest(k=250))]).fit(transformedTrainData, train_Sand_labels)
#selector = SelectFromModel(LassoCV(), threshold=0.25).fit(transformedTrainData, train_Sand_labels)
selectedTrainData = selector.transform(transformedTrainData)
selectedDevData = selector.transform(transformedDevData)
classifier = LassoLars(alpha=0.01)#LassoLars is doing very well on dev set, but horribly wrong in test set. bug or really overfitted?
classifier.fit(transformedTrainData, train_Sand_labels)
print classifier.score(transformedDevData, dev_Sand_labels)
meanSquaredError = 0.0
for i in range(len(transformedDevData)):
diff = classifier.predict(transformedDevData[i].reshape(1, -1)) - dev_Sand_labels[i]
#print str(classifier.predict(transformedDevData[i].reshape(1, -1))) + ', ' + str(dev_Sand_labels[i])
squaredDiff = diff ** 2
meanSquaredError = meanSquaredError + squaredDiff
print meanSquaredError / float(len(transformedDevData))
Content source: carlosscastro/kaggle_africa_soil
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