for key in classifier_dict:
myclassifier = classifier_dict[key]
acc = D.ClfScatter(myclassifier, title=key)
return acc # Return the last accuracy (important to get the correct answer in the TP)
if __name__ == "__main__":
# We can use this function to test the Classifier
if len(
argv
) == 1: # Use the default input and output directories if no arguments are provided
input_dir = "../public_data"
output_dir = "../results"
score_dir = "../scoring_program"
else:
input_dir = argv[1]
output_dir = argv[2]
score_dir = argv[3]
# The M2 may have prepared challenges using sometimes AutoML challenge metrics
path.append(score_dir)
from zDataManager import DataManager # The class provided by binome 1
basename = 'Iris'
D = DataManager(basename, input_dir) # Load data
print(D)
test(D)
This is an example of program that tests the PSP challenge Classifier class.
You can also find a test in the main function of the class zClassifier itself.
"""
import numpy as np
from zDataManager import DataManager
from zClassifier import Classifier, Classifier2
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import cross_val_score
from zPreprocessor import Preprocessor
input_dir = "../public_data"
output_dir = "../res"
basename = 'movies'
D = DataManager(basename, input_dir) # Load data
print D
Prepro = Preprocessor()
# Preprocess on the data and load it back into D
D.data['X_train'] = Prepro.fit_transform(D.data['X_train'], D.data['Y_train'])
D.data['X_valid'] = Prepro.transform(D.data['X_valid'])
D.data['X_test'] = Prepro.transform(D.data['X_test'])
def ebar(score, sample_num):
'''ebar calculates the error bar for the classification score (accuracy or error rate)
for sample_num examples'''
return np.sqrt(1. * score * (1 - score) / sample_num)
"""
Created on Sat Mar 11 08:04:23 2017
@author: isabelleguyon
This is an example of program that tests the Iris challenge Data Manager class.
Another style is to incorporate the test as a main function in the Data manager class itself.
"""
from zDataManager import DataManager
mypath = "../sample_code"
from sys import argv, path
from os.path import abspath
path.append(abspath(mypath))
if __name__ == "__main__":
# We can use this to run this file as a script and test the DataManager
if len(
argv
) == 1: # Use the default input and output directories if no arguments are provided
input_dir = "../public_data"
output_dir = "../res"
else:
input_dir = argv[1]
output_dir = argv[2]
print("Using input_dir: " + input_dir)
print("Using output_dir: " + output_dir)
basename = 'movies'
D = DataManager(basename, input_dir)
return self.transformer.transform(X)
if __name__ == "__main__":
# We can use this to run this file as a script and test the Preprocessor
if len(
argv
) == 1: # Use the default input and output directories if no arguments are provided
input_dir = "../public_data"
output_dir = "../results"
else:
input_dir = argv[1]
output_dir = argv[2]
basename = 'Iris'
D = DataManager(basename, input_dir) # Load data
print("*** Original data ***")
print(D)
Prepro = Preprocessor()
# Preprocess on the data and load it back into D
D.data['X_train'] = Prepro.fit_transform(D.data['X_train'],
D.data['Y_train'])
D.data['X_valid'] = Prepro.transform(D.data['X_valid'])
D.data['X_test'] = Prepro.transform(D.data['X_test'])
D.feat_name = np.array(['PC1', 'PC2'])
D.feat_type = np.array(['Numeric', 'Numeric'])
# Here show something that proves that the preprocessing worked fine
print("*** Transformed data ***")
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 11 08:04:23 2017
@author: isabelleguyon
This is an example of program that tests the Iris challenge Data Manager class.
Another style is to incorporate the test as a main function in the Data manager class itself.
"""
from zDataManager import DataManager
input_dir = "../public_data"
output_dir = "../res"
basename = 'Iris'
D = DataManager(basename, input_dir)
print D
D.DataStats('train')
D.ShowScatter(1, 2, 'train')
