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pytools.py
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pytools.py
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# coding: utf-8
# # PYTHON Common Tools for Jupyter Notebooks
# In[4]:
import pandas as pd
import matplotlib.pyplot as plt
import math
import matplotlib
import sklearn as sk
import numpy as np
from sklearn import preprocessing
# ## Plot Correlation Matrix
#
# Parameters:
# *df - Pandas dataframe to pass
# *filename - should end with .png extension
# *size - 30 is a good number for a large matris
# In[5]:
# This function does the actual graphical plotting of the correlation matrix. To see the actual correlation
# numbers, simply use this call: df_scaled.corr()
def plot_corr(df, filename, size ):
corr = df.corr()
# 30 is a good number for size
fig, ax = plt.subplots(figsize=(size, size))
cax = ax.matshow(corr)
plt.xticks(range(len(corr.columns)), corr.columns, rotation=70)
plt.yticks(range(len(corr.columns)), corr.columns)
fig.colorbar(cax)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(20)
fig.savefig(filename)
#plt.show()
# Filename should end in .png
# ## Confusion Matrix
#
# The below creates a graphical confusion matrix when the expected and predicted y values are passed with an array of target names.
#
# Here's how I create the target array:
#
# target_array = []
# test = df_t[41].drop_duplicates() # where 41 is the column where the target vector resides
# for t in test:
# target_array.append(t)
#
#
# In[4]:
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
import itertools
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
plt.colorbar()
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cm = np.round(cm, decimals=2)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.clim(0,1) # Reset the colorbar to reflect probabilities
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, cm[i, j],
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.grid('off')
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
def call_confusion_matrix(y_test, y_pred, target_array, filename, size=15):
from sklearn.metrics import confusion_matrix
#class_names = ['On-Time', 'Late']# Compute confusion matrix
class_names = target_array
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)
filename1 = ("1_%s" % filename)
filename2 = ("2_%s" % filename)
# Plot non-normalized confusion matrix
plt.figure(figsize=(size, size), dpi=200)
plot_confusion_matrix(cnf_matrix, classes=class_names,
title='Confusion matrix, without normalization')
plt.savefig(filename1, bbox_inches='tight')
# Plot normalized confusion matrix
plt.figure(figsize=(size, size), dpi=200)
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
title='Normalized confusion matrix')
plt.savefig(filename2, bbox_inches='tight')
plt.show()
# ## Cross Validation Calls for Random Forest Classifier
#
# I use this fairly often. I call it like such:
#
# cross_val_RF(model_rf, X_train, y_train)
# In[6]:
import operator
def cross_val_RF(model, X, y):
rs = RandomizedSearchCV(model, param_distributions={
'n_estimators': stats.randint(30, 200),
'max_features': ['auto', 'sqrt', 'log2'],
"max_depth": [3, None],
"max_features": stats.randint(1, 11),
"min_samples_split": stats.randint(1, 11),
"min_samples_leaf": stats.randint(1, 11),
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]})
rs.fit(X_train, y_train)
report(rs.grid_scores_)
def report(grid_scores, n_top=3):
top_scores = sorted(grid_scores, key=operator.itemgetter(1), reverse=True)[:n_top]
for i, score in enumerate(top_scores):
print("Model with rank: {0}".format(i + 1))
print("Mean validation score: {0:.3f} (std: {1:.3f})".format(
score.mean_validation_score,
np.std(score.cv_validation_scores)))
print("Parameters: {0}".format(score.parameters))
print("")
# ## Data Enumeration
#
# This is useful for automatically converting symbols to numbers for the purpose of classification.
#
# '''
# Call like such:
#
# columns_to_convert =[1,2,3,41]
#
# for col in columns_to_convert:
# enumerate_text(df, col)
#
# '''
# In[7]:
def enumerate_text(df, col):
target_array = []
test = df[col].drop_duplicates()
for t in test:
target_array.append(t)
p = [(j, i) for i, j in enumerate(target_array)]
b = dict(p)
print (b)
df[col].replace(b, inplace=True)
#df_t[:15]
return df[col]
# ## Receiver Operating Characteristic (ROC) metric
#
# Used to evaluate classifier output quality using cross-validation.
#
# ROC curves typically feature true positive rate on the Y axis, and false positive rate on the X axis. This means that the top left corner of the plot is the “ideal” point - a false positive rate of zero, and a true positive rate of one. This is not very realistic, but it does mean that a larger area under the curve (AUC) is usually better.
#
# The “steepness” of ROC curves is also important, since it is ideal to maximize the true positive rate while minimizing the false positive rate.
#
# This roughly shows how the classifier output is affected by changes in the training data, and how different the splits generated by K-fold cross-validation are from one another.
# In[2]:
def roc(model, X, y, target, filename):
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import StratifiedKFold
from itertools import cycle
from scipy import interp
cv = StratifiedKFold(n_splits=6)
y = np.array(y)
X = np.array(X)
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])
lw = 2
plt.figure(figsize=(10, 10), dpi=200)
i = 0
for (train, test), color in zip(cv.split(X, y), colors):
probas_ = model.fit(X[train], y[train]).predict_proba(X[test])
# Compute ROC curve and area the curve
fpr, tpr, thresholds = roc_curve(y[test], probas_[:, 1])
mean_tpr += interp(mean_fpr, fpr, tpr)
mean_tpr[0] = 0.0
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, lw=lw, color=color,
label='ROC fold %d (area = %0.2f)' % (i, roc_auc))
i += 1
plt.plot([0, 1], [0, 1], linestyle='--', lw=lw, color='k',
label='Luck')
mean_tpr /= cv.get_n_splits(X, y)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
plt.plot(mean_fpr, mean_tpr, color='g', linestyle='--',
label='Mean ROC (area = %0.2f)' % mean_auc, lw=lw)
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.savefig(filename, bbox_inches='tight')
plt.show()
# ## Principal Components Analysis
#
# A method to take a large number of X features and re-define them along a smaller number of principal component axes. I would use this to reduce dimensionality of a predictive set of X features down to a smaller, equally useful set of principal component vectors.
# In[3]:
from sklearn import decomposition
pca = decomposition.PCA()
def fit_features_pca(X):
from sklearn import decomposition
pca = decomposition.PCA()
pca.fit(X)
print("PCA variance by principal component\n", pca.explained_variance_)
def reduce_features_pca(n, X):
print("Shape of the original X matrix\n", X.shape)
pca.n_components = n
X_reduced = pca.fit_transform(X)
print("Shape of the reduced X matrix\n", X_reduced.shape)
return X_reduced