In [1]:

    

%matplotlib inline

import os
import json
import time
import pickle
import requests


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


    

In [2]:

    

URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data"

def fetch_data(fname='seeds_dataset.txt'):
    """
    Helper method to retreive the ML Repository dataset.
    """
    response = requests.get(URL)
    outpath  = os.path.abspath(fname)
    with open(outpath, 'wb') as f:
        f.write(response.content)
    
    return outpath

# Defining fetching data from the URL
DATA = fetch_data()


    

In [3]:

    

DATA


    

    
Out[3]:





'C:\\Users\\585160\\Documents\\Data Science\\machine-learning\\notebook\\seeds_dataset.txt'

In [4]:

    

# Labeing all the features

FEATURES  = [
    "alcohol",
    "malic acid",
    "ash",
    "alcalinity of ash",
    "magnesium",
    "total phenols",
    "flavanoids",
    "nonflavanoid phenols",
    "proanthocyanins",
    "color intensity",
    "hue",
    "OD280/OD315 of diluted wines",
    "proline",
]

LABEL_MAP = {
    1: "Cultivar1",
    2: "Cultivar2",
    3: "Cultivar3",
}

# Putting the data into a dataframe
df = pd.read_csv(DATA, header=None, names=FEATURES)

# Converting class labels into text
for k,v in LABEL_MAP.items():
    df.ix[df.alcohol == k, 'alcohol'] = v

# Describing the dataset
print(df.describe())


    

    




          alcohol  malic acid         ash  alcalinity of ash   magnesium  \
count  178.000000  178.000000  178.000000         178.000000  178.000000   
mean    13.000618    2.336348    2.366517          19.494944   99.741573   
std      0.811827    1.117146    0.274344           3.339564   14.282484   
min     11.030000    0.740000    1.360000          10.600000   70.000000   
25%     12.362500    1.602500    2.210000          17.200000   88.000000   
50%     13.050000    1.865000    2.360000          19.500000   98.000000   
75%     13.677500    3.082500    2.557500          21.500000  107.000000   
max     14.830000    5.800000    3.230000          30.000000  162.000000   

       total phenols  flavanoids  nonflavanoid phenols  proanthocyanins  \
count     178.000000  178.000000            178.000000       178.000000   
mean        2.295112    2.029270              0.361854         1.590899   
std         0.625851    0.998859              0.124453         0.572359   
min         0.980000    0.340000              0.130000         0.410000   
25%         1.742500    1.205000              0.270000         1.250000   
50%         2.355000    2.135000              0.340000         1.555000   
75%         2.800000    2.875000              0.437500         1.950000   
max         3.880000    5.080000              0.660000         3.580000   

       color intensity         hue  OD280/OD315 of diluted wines      proline  
count       178.000000  178.000000                    178.000000   178.000000  
mean          5.058090    0.957449                      2.611685   746.893258  
std           2.318286    0.228572                      0.709990   314.907474  
min           1.280000    0.480000                      1.270000   278.000000  
25%           3.220000    0.782500                      1.937500   500.500000  
50%           4.690000    0.965000                      2.780000   673.500000  
75%           6.200000    1.120000                      3.170000   985.000000  
max          13.000000    1.710000                      4.000000  1680.000000  

In [5]:

    

# Determining the shape of the data
print("{} instances with {} features\n".format(*df.shape))

# Determining the frequency of each class
print(df.groupby('alcohol')['alcohol'].count())


    

    




178 instances with 13 features

alcohol
11.03    1
11.41    1
11.45    1
11.46    1
11.56    1
11.61    1
11.62    1
11.64    1
11.65    1
11.66    1
11.76    1
11.79    1
11.81    1
11.82    2
11.84    2
11.87    1
11.96    1
12.00    3
12.04    1
12.07    1
12.08    5
12.16    1
12.17    1
12.20    1
12.21    1
12.22    1
12.25    3
12.29    4
12.33    2
12.34    1
        ..
13.75    1
13.76    1
13.77    1
13.78    1
13.82    1
13.83    2
13.84    1
13.86    2
13.87    1
13.88    2
13.90    1
13.94    1
14.02    1
14.06    2
14.10    2
14.12    1
14.13    1
14.16    1
14.19    1
14.20    1
14.21    1
14.22    2
14.23    1
14.30    1
14.34    1
14.37    1
14.38    2
14.39    1
14.75    1
14.83    1
Name: alcohol, dtype: int64

In [14]:

    

# Creating a scatter matrix of the dataframe features
from pandas.tools.plotting import scatter_matrix
scatter_matrix(df, alpha=0.2, figsize=(12, 12), diagonal='kde')
plt.show()


    

In [15]:

    

from pandas.tools.plotting import parallel_coordinates
plt.figure(figsize=(25,25))
parallel_coordinates(df, 'alcohol')
plt.show()


    

In [16]:

    

from pandas.tools.plotting import radviz
plt.figure(figsize=(12,12))
radviz(df, 'alcohol')
plt.show()


    

In [17]:

    

from sklearn.datasets.base import Bunch

DATA_DIR = os.path.abspath(os.path.join(".", "..", "data", "wine"))

# Show the contents of the data directory
for name in os.listdir(DATA_DIR):
    if name.startswith("."): continue
    print("- {}".format(name))


    

    




- dataset.csv
- meta.json
- README.md
- wine.txt

In [20]:

    

def load_data(root=DATA_DIR):
    # Construct the `Bunch` for the wine dataset
    filenames     = {
        'meta': os.path.join(root, 'meta.json'),
        'rdme': os.path.join(root, 'README.md'),
        'data': os.path.join(root, 'wine.txt'),
    }

    # Load the meta data from the meta json
    with open(filenames['meta'], 'r') as f:
        meta = json.load(f)
        target_names  = meta['target_names']
        feature_names = meta['feature_names']

    # Load the description from the README. 
    with open(filenames['rdme'], 'r') as f:
        DESCR = f.read()

    # Load the dataset from the text file.
    dataset = np.loadtxt(filenames['data'], delimiter=',')

    # Extract the target from the data
    data   = dataset[:, 0:-1]
    target = dataset[:, -1]

    # Create the bunch object
    return Bunch(
        data=data,
        target=target,
        filenames=filenames,
        target_names=target_names,
        feature_names=feature_names,
        DESCR=DESCR
    )

# Save the dataset as a variable we can use.
dataset = load_data()

print(dataset.data.shape)
print(dataset.target.shape)


    

    




(178, 13)
(178,)

In [22]:

    

# Importing the algorithms from sklearn

from sklearn import metrics
from sklearn import cross_validation
from sklearn.cross_validation import KFold

from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier


    

In [23]:

    

def fit_and_evaluate(dataset, model, label, **kwargs):
    """
    Because of the Scikit-Learn API, we can create a function to
    do all of the fit and evaluate work on our behalf!
    """
    start  = time.time() # Start the clock! 
    scores = {'precision':[], 'recall':[], 'accuracy':[], 'f1':[]}
    
    for train, test in KFold(dataset.data.shape[0], n_folds=12, shuffle=True):
        X_train, X_test = dataset.data[train], dataset.data[test]
        y_train, y_test = dataset.target[train], dataset.target[test]
        
        estimator = model(**kwargs)
        estimator.fit(X_train, y_train)
        
        expected  = y_test
        predicted = estimator.predict(X_test)
        
        # Append our scores to the tracker
        scores['precision'].append(metrics.precision_score(expected, predicted, average="weighted"))
        scores['recall'].append(metrics.recall_score(expected, predicted, average="weighted"))
        scores['accuracy'].append(metrics.accuracy_score(expected, predicted))
        scores['f1'].append(metrics.f1_score(expected, predicted, average="weighted"))

    # Report
    print("Build and Validation of {} took {:0.3f} seconds".format(label, time.time()-start))
    print("Validation scores are as follows:\n")
    print(pd.DataFrame(scores).mean())
    
    # Write official estimator to disk
    estimator = model(**kwargs)
    estimator.fit(dataset.data, dataset.target)
    
    outpath = label.lower().replace(" ", "-") + ".pickle"
    with open(outpath, 'wb') as f:
        pickle.dump(estimator, f)

    print("\nFitted model written to:\n{}".format(os.path.abspath(outpath)))


    

In [25]:

    

# Perform SVC Classification
fit_and_evaluate(dataset, SVC, "Wine SVM Classifier")


    

    




C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)






    




Build and Validation of Wine SVM Classifier took 0.585 seconds
Validation scores are as follows:

accuracy     0.016667
f1           0.009259
precision    0.007778
recall       0.016667
dtype: float64

Fitted model written to:
C:\Users\585160\Documents\Data Science\machine-learning\notebook\wine-svm-classifier.pickle

In [26]:

    

# Perform kNN Classification
fit_and_evaluate(dataset, KNeighborsClassifier, "Wine kNN Classifier", n_neighbors=12)


    

    




Build and Validation of Wine kNN Classifier took 0.094 seconds
Validation scores are as follows:

accuracy     0.0
f1           0.0
precision    0.0
recall       0.0
dtype: float64

Fitted model written to:
C:\Users\585160\Documents\Data Science\machine-learning\notebook\wine-knn-classifier.pickle






    




C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)

In [27]:

    

# Perform Random Forest Classification
fit_and_evaluate(dataset, RandomForestClassifier, "Wine Forest Classifier")


    

    




C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)






    




Build and Validation of Wine Forest Classifier took 0.470 seconds
Validation scores are as follows:

accuracy     0.011111
f1           0.012963
precision    0.016667
recall       0.011111
dtype: float64

Fitted model written to:
C:\Users\585160\Documents\Data Science\machine-learning\notebook\wine-forest-classifier.pickle

In [28]:

    

# Lesson learned - none of these are good to use. Let's try Naive Bayes.

from sklearn.naive_bayes import GaussianNB


    

In [29]:

    

# Perform Naive Bayes

fit_and_evaluate(dataset, GaussianNB, "Wine Bayes Classifier" )


    

    




C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)






    




Build and Validation of Wine Bayes Classifier took 0.230 seconds
Validation scores are as follows:

accuracy     0.011111
f1           0.011111
precision    0.011111
recall       0.011111
dtype: float64

Fitted model written to:
C:\Users\585160\Documents\Data Science\machine-learning\notebook\wine-bayes-classifier.pickle

In [30]:

    

# Let's try LDA

from sklearn.lda import LDA


    

    




C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\lda.py:4: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19
  "in 0.17 and will be removed in 0.19", DeprecationWarning)

In [31]:

    

fit_and_evaluate(dataset, LDA, "Wine LDA Classifier")


    

    




Build and Validation of Wine LDA Classifier took 0.195 seconds
Validation scores are as follows:

accuracy     0.005556
f1           0.005556
precision    0.005556
recall       0.005556
dtype: float64

Fitted model written to:
C:\Users\585160\Documents\Data Science\machine-learning\notebook\wine-lda-classifier.pickle






    




C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\discriminant_analysis.py:453: UserWarning: The priors do not sum to 1. Renormalizing
  UserWarning)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)

In [32]:

    

# This is even worse. Let's try Logistic Regression

from sklearn.linear_model import LogisticRegression


    

In [34]:

    

# Performing Logistic Regression

fit_and_evaluate(dataset, LogisticRegression, "Wine Logistic Regression Classifier")


    

    




C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1074: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
  'precision', 'predicted', average, warn_for)
C:\Users\585160\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\metrics\classification.py:1076: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no true samples.
  'recall', 'true', average, warn_for)






    




Build and Validation of Wine Logistic Regression Classifier took 0.808 seconds
Validation scores are as follows:

accuracy     0.011111
f1           0.011111
precision    0.011111
recall       0.011111
dtype: float64

Fitted model written to:
C:\Users\585160\Documents\Data Science\machine-learning\notebook\wine-logistic-regression-classifier.pickle

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