In [21]:

    

import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
from datetime import datetime
import seaborn as sns
%matplotlib inline
from sklearn.base import BaseEstimator, TransformerMixin
class DataFrameSelector(BaseEstimator, TransformerMixin):
    def __init__(self, attribute_names):
        self.attribute_names = attribute_names
    def fit(self, X, y=None):
        return self
    def transform(self, X):
        return X[self.attribute_names].values


    

In [284]:

    

i = 3
diffRMSD = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/diffRMSD.txt", names=["diffRMSD"], sep="\s+")
RMSD = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/individualrmsds.txt", names=["RMSD"], sep="\s+")
eigenvalues = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/eigenvalues.txt", names=["eigenvalues"], sep="\s+")
entropy = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/entropy.txt", names=["entropy"], sep="\s+")
pca = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/std.PCA.txt", names=["pca"], sep="\s+")
raw_data = pd.concat([eigenvalues, entropy, pca, diffRMSD,RMSD], axis=1)


    

In [285]:

    

raw_data.plot.scatter("diffRMSD", "RMSD")


    

    
Out[285]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2243b898>






    

In [263]:

    

raw_data.plot.scatter("diffRMSD", "eigenvalues")


    

    
Out[263]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a1ffbf160>






    

In [265]:

    

raw_data.plot.scatter("diffRMSD", "entropy")


    

    
Out[265]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a21060ac8>






    

In [261]:

    

raw_data.plot.scatter("diffRMSD", "pca")


    

    
Out[261]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a216dcef0>






    

In [209]:

    

raw_data.plot.scatter("eigenvalues", "entropy")


    

    
Out[209]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2172b978>






    

In [260]:

    

raw_data.plot.scatter("eigenvalues", "pca")


    

    
Out[260]:





<matplotlib.axes._subplots.AxesSubplot at 0x1a2163e8d0>






    

In [293]:

    

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import PolynomialFeatures
def my_transform(data, label, degree, FEATURES=FEATURES):

    # LABEL = "Qw"
    LABEL = label
    PolynomialDegree = degree

    num_attribs = FEATURES
    cat_attribs = [LABEL]
    num_pipeline = Pipeline([
            ('selector', DataFrameSelector(num_attribs)),
            ('std_scaler', StandardScaler()),
            ('poly', PolynomialFeatures(degree=PolynomialDegree, include_bias=False))
        ])
    cat_pipeline = Pipeline([
            ('selector', DataFrameSelector(cat_attribs))
        ])

    full_pipeline = FeatureUnion(transformer_list=[
            ("num_pipeline", num_pipeline),
            ("cat_pipeline", cat_pipeline),
        ])
    return full_pipeline.fit_transform(data)


    

In [306]:

    

# FEATURES = ["eigenvalues", "entropy", "pca"]
# FEATURES = ["eigenvalues", "entropy", "diffRMSD"]
FEATURES = ["eigenvalues", "entropy"]

# FEATURES = ["eigenvalues"]
LABEL = "diffRMSD"
LABEL = "RMSD"
data = my_transform(raw_data, label=LABEL, degree=1, FEATURES=FEATURES)


    

In [307]:

    

from sklearn.linear_model import LinearRegression


    

In [308]:

    

lin_reg = LinearRegression()
train_y = data[:,-1]
train_set = data[:,:-1]

lin_reg.fit(train_set, train_y)
y_pred = lin_reg.predict(train_set)


    

In [309]:

    

plt.plot(y_pred, train_y, ".")
print("mean square error:", np.sum((train_y-y_pred)**2)/len(y_pred))


    

    




mean square error: 0.00157650707685






    

In [303]:

    

plt.plot(y_pred, train_y, ".")
print("mean square error:", np.sum((train_y-y_pred)**2)/len(y_pred))


    

    




mean square error: 0.00211314538652






    

In [310]:

    

cutoff = 10
a = pd.DataFrame(y_pred, columns=["pred"])
a["large"] = a["pred"].rank(method="first") <= cutoff

b = pd.DataFrame(train_y, columns=["train"])
b["gt"] = b["train"].rank(method="first") <= cutoff # ground truth


    

In [311]:

    

picked = pd.concat([a,b], axis=1).query("large == True and gt == True")
print("out of the top 10, picked out ", picked.shape[0])


    

    




out of the top 10, picked out  5

In [305]:

    

picked = pd.concat([a,b], axis=1).query("large == True and gt == True")
print("out of the top 10, picked out ", picked.shape[0])


    

    




out of the top 10, picked out  5

In [252]:

    

picked.sum()


    

    
Out[252]:





pred    -1.596755
large    8.000000
train   -1.629867
gt       8.000000
dtype: float64

In [ ]:

    




    

In [221]:

    

pd.concat([a,b], axis=1).sum()


    

    
Out[221]:





pred     -4.31886
large    10.00000
train    -4.31886
gt       10.00000
dtype: float64

In [194]:

    

pd.concat([a,b], axis=1).query("large == True")


    

    
Out[194]:







  

    

      

      
pred
      
large
      
train
      
gt
    
  
  

    

      
0
      
-0.757080
      
True
      
-0.814549
      
True
    
    

      
1
      
-0.399522
      
True
      
-0.457784
      
True
    
    

      
2
      
-0.323967
      
True
      
-0.095139
      
True
    
    

      
3
      
-0.237361
      
True
      
-0.559770
      
True
    
    

      
4
      
-0.188904
      
True
      
-0.168058
      
True
    
    

      
5
      
-0.174447
      
True
      
-0.013000
      
False
    
    

      
6
      
-0.152443
      
True
      
-0.028691
      
False
    
    

      
7
      
-0.141327
      
True
      
-0.006070
      
False
    
    

      
8
      
-0.110571
      
True
      
-0.096960
      
True
    
    

      
9
      
-0.097252
      
True
      
-0.565082
      
True
    
  

In [195]:

    

picked.sum()


    

    
Out[195]:





pred    -2.114658
large    7.000000
train   -2.757341
gt       7.000000
dtype: float64

In [165]:

    

i = 2
diffRMSD = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/diffRMSD.txt", names=["diffRMSD"], sep="\s+")
eigenvalues = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/eigenvalues.txt", names=["eigenvalues"], sep="\s+")
entropy = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/entropy.txt", names=["entropy"], sep="\s+")
pca = pd.read_table(f"/Users/weilu/Research/data/refinement_jul27/set{i}/std.PCA.txt", names=["pca"], sep="\s+")
raw_data_2 = pd.concat([eigenvalues, entropy, pca, diffRMSD], axis=1)


    

In [166]:

    

data_2 = my_transform(raw_data, label=LABEL, degree=1, FEATURES=FEATURES)


    

In [167]:

    

test_y = data_2[:,-1]
test_set = data_2[:,:-1]

y_pred = lin_reg.predict(train_set)


    

In [168]:

    

data_2


    

    
Out[168]:





array([[  6.74157155e+00,   1.67029682e-02,  -1.31131000e-01],
       [  6.24683413e+00,  -4.61862619e+00,  -7.36925000e-02],
       [  4.63557806e+00,  -3.54300231e-01,  -4.41102000e-02],
       [  4.28502428e+00,  -1.91024887e+00,  -4.68995000e-03],
       [  3.92727394e+00,   1.08402967e+00,  -3.46758000e-02],
       [  3.58305324e+00,   9.74195694e-01,  -2.50843000e-02],
       [  3.26352572e+00,   2.06149694e-01,  -1.01665000e-02],
       [  3.18244367e+00,   3.88927742e-01,  -6.82601000e-05],
       [  2.91077060e+00,   4.19328449e-01,  -9.05235000e-05],
       [  2.76889872e+00,   7.60120407e-02,  -5.62739000e-02],
       [  2.62258098e+00,   1.23791533e+00,  -1.53555000e-02],
       [  2.43144532e+00,  -9.26596297e-02,  -6.22972000e-04],
       [  2.34912812e+00,   1.44628764e+00,  -4.32242000e-02],
       [  2.08882973e+00,   2.72636079e-01,  -8.62511000e-02],
       [  2.07284963e+00,   5.23852719e-01,  -5.44797000e-04],
       [  2.03651007e+00,  -9.45072730e-02,  -4.29566000e-02],
       [  1.94714519e+00,  -2.33640544e-02,  -4.71649000e-03],
       [  1.92257582e+00,   2.01383420e-02,  -7.33374000e-02],
       [  1.86818930e+00,  -6.55999061e-02,  -2.36041000e-02],
       [  1.80240997e+00,  -2.40728320e-01,  -7.04469000e-03],
       [  1.67816871e+00,   1.94638276e-01,  -1.39979000e-03],
       [  1.61222363e+00,   4.50298545e-01,  -8.72481000e-03],
       [  1.53990975e+00,   4.05572359e-01,  -7.86921000e-03],
       [  1.46143075e+00,   3.78978043e-01,  -4.90715000e-02],
       [  1.42182788e+00,  -3.61165485e-01,  -7.81370000e-02],
       [  1.32038959e+00,   1.09116305e+00,  -3.08933000e-03],
       [  1.27678537e+00,   7.37054083e-01,  -1.50127000e-03],
       [  1.23183793e+00,   8.62865670e-01,  -1.45141000e-02],
       [  1.13187994e+00,   8.89337333e-01,  -8.65393000e-02],
       [  1.12354823e+00,   1.60001880e-01,  -1.68806000e-02],
       [  1.08904266e+00,   1.08542990e+00,  -7.58387000e-03],
       [  1.07635577e+00,   4.32038290e-01,  -6.07691000e-02],
       [  1.02425211e+00,   1.10223324e+00,  -8.81419000e-03],
       [  9.38925295e-01,   1.14661351e+00,  -6.69598000e-03],
       [  9.00334763e-01,   8.88459703e-01,  -7.90080000e-03],
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       [  2.84025445e-01,   3.88454581e-01,  -3.14038000e-02],
       [  2.73161638e-01,   9.14106503e-01,  -2.58712000e-02],
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       [  1.29290885e-01,   9.32317355e-01,  -2.43820000e-03],
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       [  1.11873846e-01,   1.00081972e+00,  -3.48448000e-02],
       [  1.06781126e-01,   8.52341895e-01,  -9.83258000e-04],
       [  9.93884951e-02,   8.38245166e-01,  -7.55218000e-02],
       [  8.96743032e-02,   7.76455773e-01,  -9.22948000e-03],
       [  8.12414171e-02,   8.94644408e-01,  -2.36153000e-03],
       [  7.62726349e-02,   7.76115041e-01,  -1.04692000e-01],
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       [  5.43014891e-02,   5.45415324e-01,  -6.30976000e-03],
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       [  3.02433207e-02,   4.88215642e-01,  -7.03305000e-05],
       [  7.93547228e-03,   5.69887401e-01,  -3.17742000e-02],
       [  4.81539068e-03,   5.71668292e-01,  -5.30854000e-03],
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       [ -1.94667798e-01,   7.04915993e-01,  -1.34197000e-04],
       [ -2.01254775e-01,   9.01191070e-01,  -1.15387000e-03],
       [ -2.02683900e-01,   1.00603328e+00,  -4.59827000e-03],
       [ -2.09524272e-01,   7.64426862e-01,  -2.19924000e-02],
       [ -2.16744830e-01,   5.95908175e-01,  -1.22218000e-03],
       [ -2.26332604e-01,   9.10303996e-01,  -2.18137000e-05],
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       [ -2.36004863e-01,   4.82250719e-01,  -5.92800000e-03],
       [ -2.37595981e-01,   4.42122577e-01,  -1.10518000e-02],
       [ -2.39728782e-01,   3.76845825e-01,  -3.34106000e-02],
       [ -2.45324850e-01,   5.19256591e-01,  -2.45945000e-05],
       [ -2.47010658e-01,   3.69875004e-01,  -9.76754000e-04],
       [ -2.48309270e-01,   7.86047858e-01,  -1.62749000e-03],
       [ -2.52582130e-01,   9.01886058e-02,  -4.90756000e-04],
       [ -2.55046175e-01,   3.67274312e-01,  -7.78287000e-04],
       [ -2.59839814e-01,   6.80555430e-01,  -7.15732000e-03],
       [ -2.60837453e-01,   7.66702237e-01,  -2.08928000e-02],
       [ -2.62514701e-01,   1.27169139e-01,  -3.52615000e-04],
       [ -2.70285968e-01,   7.11385815e-01,  -1.09640000e-02],
       [ -2.73468080e-01,   3.89220182e-01,  -3.34225000e-05],
       [ -2.78059406e-01,   6.49654791e-01,  -3.79924000e-03],
       [ -2.81705973e-01,   7.03215516e-01,  -2.16499000e-02],
       [ -2.85682574e-01,  -7.39062395e-01,  -1.19337000e-03],
       [ -2.89661192e-01,   5.44645460e-01,  -2.66348000e-04],
       [ -2.93333998e-01,  -1.86240380e-01,  -5.18529000e-04],
       [ -2.94835543e-01,   6.54997295e-01,  -5.36594000e-04],
       [ -2.96985682e-01,   4.18970899e-01,  -1.60224000e-04],
       [ -2.97785878e-01,   7.11501051e-01,  -3.69685000e-04],
       [ -3.03260210e-01,   8.77836218e-01,  -1.84768000e-04],
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In [ ]: