Merge pull request #16 from bjherger/example

Examples

examples/mushrooms.py

@@ -0,0 +1,33 @@
+import logging
+
+from keras import Model
+from keras.layers import Dense
+
+from keras_pandas.Automater import Automater
+from keras_pandas.lib import load_mushrooms, load_titanic
+
+
+def main():
+    logging.getLogger().setLevel(logging.DEBUG)
+
+    observations = load_mushrooms()
+
+    # Transform the data set, using keras_pandas
+    auto = Automater(categorical_vars=observations.columns, response_var='class')
+    X, y = auto.fit_transform(observations)
+
+    # Create model
+    x = auto.input_nub
+    x = Dense(30)(x)
+    x = auto.output_nub(x)
+
+    model = Model(inputs=auto.input_layers, outputs=x)
+    model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+    # Train model
+    model.fit(X, y, epochs=10, validation_split=.5)
+
+    pass
+
+if __name__ == '__main__':
+    main()

examples/titanic.py

@@ -0,0 +1,36 @@
+import logging
+
+from keras import Model
+from keras.layers import Dense
+
+from keras_pandas.Automater import Automater
+from keras_pandas.lib import load_mushrooms, load_titanic
+
+
+def main():
+    logging.getLogger().setLevel(logging.DEBUG)
+
+    observations = load_titanic()
+
+    # Transform the data set, using keras_pandas
+    categorical_vars = ['pclass', 'sex', 'survived']
+    numerical_vars = ['age', 'siblings_spouses_aboard', 'parents_children_aboard', 'fare']
+
+    auto = Automater(categorical_vars=categorical_vars, numerical_vars=numerical_vars, response_var='survived')
+    X, y = auto.fit_transform(observations)
+
+    # Create model
+    x = auto.input_nub
+    x = Dense(30)(x)
+    x = auto.output_nub(x)
+
+    model = Model(inputs=auto.input_layers, outputs=x)
+    model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
+
+    # Train model
+    model.fit(X, y, epochs=10, validation_split=.5)
+
+    pass
+
+if __name__ == '__main__':
+    main()

keras_pandas/Automater.py

@@ -86,8 +86,11 @@ def fit(self, input_dataframe):
 
     def transform(self, dataframe):
 
-        # Check if we have a response variable, and if it is available
+        # Check if fitted yet
+        if not self.fitted:
+            raise ValueError('Cannot transform without being fitted first. Call fit() method before transform() method')
 
+        # Check if we have a response variable, and if it is available
         if self.response_var is not None and self.response_var in dataframe.columns:
             y_available = True
         else:

keras_pandas/lib.py

@@ -1,3 +1,10 @@
+import logging
+import os
+import pandas
+
+import requests
+
+
 def check_variable_list_are_valid(variable_type_dict):
     """
 
@@ -19,3 +26,67 @@ def check_variable_list_are_valid(variable_type_dict):
 
     return True
 
+def download_file(url, local_file_path, filename):
+    """
+    Download the file at `url` in chunks, to the location at `local_file_path`
+    :param url: URL to a file to be downloaded
+    :type url: str
+    :param local_file_path: Path to download the file to
+    :type local_file_path: str
+    :return: The path to the file on the local machine (same as input `local_file_path`)
+    :rtype: str
+    """
+    logging.info('Downloading file from url: {}, to path: {}'.format(url, local_file_path))
+    # Reference variables
+    chunk_count = 0
+    local_file_path = os.path.expanduser(local_file_path)
+    if not os.path.exists(local_file_path):
+        os.makedirs(local_file_path)
+
+    local_file_path = os.path.join(local_file_path, filename)
+
+    # Create connection to the stream
+    r = requests.get(url, stream=True)
+
+    # Open output file
+    if not os.path.exists(local_file_path):
+        with open(local_file_path, 'wb') as f:
+
+            # Iterate through chunks of file
+            for chunk in r.iter_content(chunk_size=1048576):
+
+                logging.debug('Downloading chunk: {} for file: {}'.format(chunk_count, local_file_path))
+
+                # If there is a chunk to write to file, write it
+                if chunk:
+                    f.write(chunk)
+
+                # Increase chunk counter
+                chunk_count = chunk_count + 1
+
+        r.close()
+    return local_file_path
+
+def load_mushrooms():
+    # Extract the data
+    file_path = download_file(
+        'https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data',
+        '~/.keras-pandas/example_datasets/', filename='agaricus-lepiota.data')
+
+    observations = pandas.read_csv(filepath_or_buffer=file_path,
+                                   names=['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
+                                          'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
+                                          'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
+                                          'stalk-surface-below-ring', 'stalk-color-above-ring',
+                                          'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',
+                                          'ring-type', 'spore-print-color', 'population', 'habitat'])
+    return observations
+
+def load_titanic():
+    file_path = download_file('http://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv',
+                              '~/.keras-pandas/example_datasets/', filename='titanic.csv')
+
+    observations = pandas.read_csv(file_path)
+    observations.columns = map(lambda x: x.lower().replace(' ', '_').replace('/', '_'), observations.columns)
+
+    return observations

requirements.txt

@@ -1,17 +1,44 @@
+absl-py==0.2.0
+astor==0.6.2
 attrs==17.4.0
+backports.weakref==1.0.post1
+bleach==1.5.0
 certifi==2018.4.16
+chardet==3.0.4
+click==6.7
+enum34==1.1.6
+Flask==0.12.2
 funcsigs==1.0.2
+futures==3.2.0
+gast==0.2.0
+grpcio==1.11.0
+h5py==2.7.1
+html5lib==0.9999999
+idna==2.6
+itsdangerous==0.24
+Jinja2==2.10
 Keras==2.1.5
+Markdown==2.6.11
+MarkupSafe==1.0
+mock==2.0.0
 more-itertools==4.1.0
 numpy==1.14.2
 pandas==0.22.0
+pbr==4.0.2
 pluggy==0.6.0
+protobuf==3.5.2.post1
 py==1.5.3
 python-dateutil==2.7.2
 pytz==2018.4
 PyYAML==3.12
+requests==2.18.4
 scikit-learn==0.19.1
 scipy==1.0.1
 six==1.11.0
 sklearn==0.0
 sklearn-pandas==1.6.0
+tensorboard==1.7.0
+tensorflow==1.7.0
+termcolor==1.1.0
+urllib3==1.22
+Werkzeug==0.14.1