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In [2]:



    


import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report

instances = [
    {'plays fetch': True, 'species': 'Dog'},
    {'plays fetch': False, 'species': 'Dog'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Dog'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': True, 'species': 'Dog'},
    {'plays fetch': True, 'species': 'Dog'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': True, 'species': 'Cat'},
    {'plays fetch': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)

X_train = [[1] if a else [0] for a in df['plays fetch']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['plays fetch']

clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)

print (X_train)
clf.fit(X_train, y_train)

f = 'tree.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Figures 05_02 #################
"""

"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report

instances = [
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': True, 'species': 'Dog'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': False, 'species': 'Cat'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': False, 'species': 'Cat'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['is grumpy']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['is grumpy']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)

f = '/home/moonbury/notebook/result/is-grumpy.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Figures 05_03 #################
"""

"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report

instances = [
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = df[['favorite food']]

vectorizer = DictVectorizer()
X_train = [[1] if a else [0] for a in df['favorite food']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]

labels = ['favorite food=cat food']

clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
print (X_train)
clf.fit(X_train, y_train)

f = '/home/moonbury/notebook/result/favorite-food-cat-food.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Figures 05_04 #################
"""

"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report

instances = [
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['favorite food']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['favorite food=dog food']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)

f = '/home/moonbury/notebook/result/favorite-food-dog-food.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Figures 05_05 #################
"""

"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer
from sklearn.metrics import classification_report

instances = [
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['favorite food']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['favorite food=bacon']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)

f = '/home/moonbury/notebook/result/favorite-food-bacon.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the left child for animals that like to play fetch
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer

instances = [
    {'plays fetch': True, 'species': 'Dog'},
    {'plays fetch': False, 'species': 'Dog'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Dog'},
    {'plays fetch': True, 'species': 'Dog'},
    {'plays fetch': True, 'species': 'Dog'},
    {'plays fetch': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['plays fetch']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['plays fetch']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)
f = '/home/moonbury/notebook/result/level2-play-fetch.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the left child for animals that are grumpy
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer

instances = [
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': True, 'species': 'Dog'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': True, 'species': 'Cat'},
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': False, 'species': 'Dog'},
    {'is grumpy': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['is grumpy']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['is grumpy']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)
f = '/home/moonbury/notebook/result/level2-is-grumpy.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the left child for animals like dog food
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from sklearn.feature_extraction import DictVectorizer

instances = [
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['favorite food']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['favorite food=dog food']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)
f = '/home/moonbury/notebook/result/level2-favorite-food-dog-food.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the left child for animals like bacon
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz

instances = [
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['favorite food']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['favorite food=bacon']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)
f = '/home/moonbury/notebook/result/level2-favorite-food-bacon.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the right grandchild for favorite food=bacon
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz

instances = [
    {'favorite food': False, 'species': 'Dog'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Cat'},
    {'favorite food': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['favorite food']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['favorite food=bacon']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)
f = '/home/moonbury/notebook/result/level3-favorite-food-bacon.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the right grandchild for favorite food=dog food
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz

instances = [
    {'favorite food': True, 'species': 'Dog'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Cat'},
    {'favorite food': False, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['favorite food']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['favorite food=dog food']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)
f = '/home/moonbury/notebook/result/level3-favorite-food-dog-food.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the right grandchild for plays fetch
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier, export_graphviz

instances = [
    {'plays fetch': False, 'species': 'Dog'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': False, 'species': 'Cat'},
    {'plays fetch': True, 'species': 'Dog'}
]
df = pd.DataFrame(instances)
X_train = [[1] if a else [0] for a in df['plays fetch']]
y_train = [1 if d == 'Dog' else 0 for d in df['species']]
labels = ['plays fetch']
clf = DecisionTreeClassifier(max_depth=None, max_features=None, criterion='entropy',
                             min_samples_leaf=1, min_samples_split=2)
clf.fit(X_train, y_train)
f = '/home/moonbury/notebook/result/level3-plays-fetch.dot'
export_graphviz(clf, out_file=f, feature_names=labels) #, close=True)


################# Data #################
"""
Test the right grandchild for plays fetch
"""


################# Sample 1: Ad classification with Decision Trees #################
"""


Fitting 3 folds for each of 27 candidates, totalling 81 fits
[Parallel(n_jobs=-1)]: Done   1 jobs       | elapsed:    1.7s
[Parallel(n_jobs=-1)]: Done  50 jobs       | elapsed:   15.0s
[Parallel(n_jobs=-1)]: Done  71 out of  81 | elapsed:   20.7s remaining:    2.9s
[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:   23.3s finished
Best score: 0.878
Best parameters set:
	clf__max_depth: 155
	clf__min_samples_leaf: 2
	clf__min_samples_split: 1
             precision    recall  f1-score   support

          0       0.97      0.99      0.98       710
          1       0.92      0.81      0.86       110

avg / total       0.96      0.96      0.96       820

0.964634146341
"""
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV

if __name__ == '__main__':
    df = pd.read_csv('data/ad.data', header=None)
    explanatory_variable_columns = set(df.columns.values)
    response_variable_column = df[len(df.columns.values)-1]
    # The last column describes the targets
    explanatory_variable_columns.remove(len(df.columns.values)-1)

    y = [1 if e == 'ad.' else 0 for e in response_variable_column]
    X = df[list(explanatory_variable_columns)]
    X.replace(to_replace=' *\?', value=-1, regex=True, inplace=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    pipeline = Pipeline([
        ('clf', DecisionTreeClassifier(criterion='entropy'))
    ])
    parameters = {
        'clf__max_depth': (150, 155, 160),
        'clf__min_samples_split': (1, 2, 3),
        'clf__min_samples_leaf': (1, 2, 3)
    }

    grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, scoring='f1')
    grid_search.fit(X_train, y_train)
    print ('Best score: %0.3f' % grid_search.best_score_)
    print ('Best parameters set:')
    best_parameters = grid_search.best_estimator_.get_params()
    for param_name in sorted(parameters.keys()):
        print ('\t%s: %r' % (param_name, best_parameters[param_name]))

    predictions = grid_search.predict(X_test)
    print (classification_report(y_test, predictions))
    print (grid_search.score(X_test, y_test))


################# Sample 2: Ad classification with Random Forests #################
"""

"""
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV

if __name__ == '__main__':
    df = pd.read_csv('data/ad.data', header=None)
    explanatory_variable_columns = set(df.columns.values)
    response_variable_column = df[len(df.columns.values)-1]
    # The last column describes the targets
    explanatory_variable_columns.remove(len(df.columns.values)-1)

    y = [1 if e == 'ad.' else 0 for e in response_variable_column]
    X = df[list(explanatory_variable_columns)]
    X.replace(to_replace=' *\?', value=-1, regex=True, inplace=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    pipeline = Pipeline([
        ('clf', RandomForestClassifier(criterion='entropy'))
    ])
    parameters = {
        'clf__n_estimators': (5, 10, 20, 50),
        'clf__max_depth': (50, 150, 250),
        'clf__min_samples_split': (1, 2, 3),
        'clf__min_samples_leaf': (1, 2, 3)
    }

    grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, scoring='f1')
    grid_search.fit(X_train, y_train)
    print ('Best score: %0.3f' % grid_search.best_score_)
    print ('Best parameters set:')
    best_parameters = grid_search.best_estimator_.get_params()
    for param_name in sorted(parameters.keys()):
        print ('\t%s: %r' % (param_name, best_parameters[param_name]))

    predictions = grid_search.predict(X_test)
    print (classification_report(y_test, predictions))



    


















    






[[1], [0], [0], [0], [0], [0], [0], [0], [0], [1], [1], [0], [1], [1]]
[[0], [0], [1], [0], [1], [0], [1], [0], [1], [0], [0], [1], [1], [0]]










    






/usr/local/lib/python3.4/dist-packages/pandas/io/parsers.py:1139: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.
  data = self._reader.read(nrows)
/usr/local/lib/python3.4/dist-packages/ipykernel/__main__.py:424: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame










    






Fitting 3 folds for each of 27 candidates, totalling 81 fits










    






[Parallel(n_jobs=-1)]: Done   1 jobs       | elapsed:    1.9s
[Parallel(n_jobs=-1)]: Done  50 jobs       | elapsed:   47.3s
[Parallel(n_jobs=-1)]: Done  79 out of  81 | elapsed:  1.2min remaining:    1.9s
[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:  1.3min finished










    






Best score: 0.877
Best parameters set:
	clf__max_depth: 155
	clf__min_samples_leaf: 1
	clf__min_samples_split: 3
             precision    recall  f1-score   support

          0       0.99      0.98      0.98       709
          1       0.86      0.91      0.88       111

avg / total       0.97      0.97      0.97       820

0.967073170732










    






/usr/local/lib/python3.4/dist-packages/pandas/io/parsers.py:1139: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.
  data = self._reader.read(nrows)
/usr/local/lib/python3.4/dist-packages/ipykernel/__main__.py:469: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame










    






Fitting 3 folds for each of 108 candidates, totalling 324 fits










    






[Parallel(n_jobs=-1)]: Done   1 jobs       | elapsed:    1.5s
[Parallel(n_jobs=-1)]: Done  50 jobs       | elapsed:   42.7s
[Parallel(n_jobs=-1)]: Done 200 jobs       | elapsed:  2.8min
[Parallel(n_jobs=-1)]: Done 322 out of 324 | elapsed:  4.5min remaining:    1.7s
[Parallel(n_jobs=-1)]: Done 324 out of 324 | elapsed:  4.5min finished










    






Best score: 0.933
Best parameters set:
	clf__max_depth: 50
	clf__min_samples_leaf: 1
	clf__min_samples_split: 1
	clf__n_estimators: 50
             precision    recall  f1-score   support

          0       0.98      1.00      0.99       706
          1       0.98      0.87      0.92       114

avg / total       0.98      0.98      0.98       820


















In [ ]:

Content source: moonbury/pythonanywhere

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