def create_timing_curve(estimator, dataset, data_name, clf_name):
"""Generates a timing curve for the specified estimator, saves tabular
results to CSV and saves a plot of the timing curve.
Args:
estimator (object): Target classifier.
dataset(pandas.DataFrame): Source data set.
data_name (str): Name of data set being tested.
clf_name (str): Type of algorithm.
"""
# set training sizes and intervals
train_sizes = np.arange(0.1, 1.0, 0.05)
# initialise variables
train_time = []
predict_time = []
df_final = []
# iterate through training sizes and capture training and predict times
for i, train_data in enumerate(train_sizes):
X_train, X_test, y_train, y_test = split_data(dataset,
test_size=1 - train_data)
start_train = timeit.default_timer()
estimator.fit(X_train, y_train)
end_train = timeit.default_timer()
estimator.predict(X_test)
end_predict = timeit.default_timer()
train_time.append(end_train - start_train)
predict_time.append(end_predict - end_train)
df_final.append([train_data, train_time[i], predict_time[i]])
# save timing results to CSV
timedata = pd.DataFrame(
data=df_final,
columns=['Training Data Percentage', 'Train Time', 'Test Time'],
)
resdir = 'results'
res_tgt = '{}/{}'.format(resdir, clf_name)
timefile = get_abspath('{}_timing_curve.csv'.format(data_name), res_tgt)
timedata.to_csv(timefile, index=False)
# generate timing curve plot
plt.figure(2)
plt.plot(train_sizes, train_time, marker='.', color='b', label='Train')
plt.plot(train_sizes, predict_time, marker='.', color='g', label='Predict')
plt.legend(loc='best')
plt.grid(linestyle='dotted')
plt.xlabel('Samples used for training as a percentage of total')
plt.ylabel('Elapsed user time in seconds')
# save timing curve plot as PNG
plotdir = 'plots'
plt.title("Timing Curve with {} on {}".format(clf_name, data_name))
plot_tgt = '{}/{}'.format(plotdir, clf_name)
plotpath = get_abspath('{}_TC.png'.format(data_name), plot_tgt)
plt.savefig(plotpath)
plt.close()
def create_timing_curve(estimator, dataset, data_name, clf_name):
# set training sizes and intervals
train_sizes = np.arange(0.01, 1.0, 0.03)
# initialise variables
train_time = []
predict_time = []
df_final = []
# iterate through training sizes and capture training and predict times
for i, train_data in enumerate(train_sizes):
X_train, X_test, y_train, y_test = split_data(dataset,
test_size=1 - train_data)
start_train = timeit.default_timer()
estimator.fit(X_train, y_train)
end_train = timeit.default_timer()
estimator.predict(X_test)
end_predict = timeit.default_timer()
train_time.append(end_train - start_train)
predict_time.append(end_predict - end_train)
df_final.append([train_data, train_time[i], predict_time[i]])
# save timing results to CSV
timedata = pd.DataFrame(
data=df_final,
columns=['Training Data Percentage', 'Train Time', 'Test Time'])
resdir = 'results'
res_tgt = '{}/{}'.format(resdir, clf_name)
timefile = get_abspath('{}_timing_curve.csv'.format(data_name), res_tgt)
timedata.to_csv(timefile, index=False)
# generate timing curve plot
plt.figure()
plt.title("Timing Curve ({})".format(data_name))
plt.grid()
plt.plot(train_sizes, train_time, marker='.', color='y', label='Train')
plt.plot(train_sizes,
predict_time,
marker='.',
color='dodgerblue',
label='Predict')
plt.legend(loc='best')
plt.xlabel('Training Set Size (%)')
plt.ylabel('Elapsed user time in seconds')
# save timing curve plot as PNG
plotdir = 'plots'
plot_tgt = '{}/{}'.format(plotdir, clf_name)
plotpath = get_abspath('{}_TC.png'.format(data_name), plot_tgt)
plt.savefig(plotpath)
plt.close()
# estimators with iteration param
iterators = {'Boosting': 'ADA__n_estimators', 'ANN': 'MLP__max_iter'}
# validation curve parameter names and ranges
vc_params = {
'DT': ('DT__max_depth', np.arange(1, 50, 1)),
'KNN': ('KNN__n_neighbors', np.arange(1, 50, 1)),
'ANN': ('MLP__hidden_layer_sizes', np.arange(1, 50, 5)),
'SVM_RBF': ('SVMR__gamma', np.logspace(-9, -1, 15)),
'SVM_PLY': ('SVMP__gamma', np.logspace(-9, -1, 20)),
'Boosting': ('ADA__n_estimators', np.arange(20, 200, 10))
}
# start model evaluation loop
for df in dnames:
X_train, X_test, y_train, y_test = split_data(dfs[df])
# load pickled models into estimators dict
for m in mnames:
mfile = '{}\{}_grid.pkl'.format(m, df)
model = load_pickled_model(get_abspath(mfile, filepath='models'))
estimators[m] = model
log_cols2 = [
"Train Set Accuracy", "Cross-Validation Score",
"Test Set Accuracy"
]
log2 = pd.DataFrame(columns=log_cols2)
# generate validation, learning, and timing curves
for name, estimator in estimators.iteritems():
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import os
import seaborn as sns
# Importing the dataset
p_abalone = get_abspath('abalone.csv', 'data/experiments')
p_abalone2 = get_abspath('abalone-2.csv', 'data/experiments')
p_seismic = get_abspath('seismic-bumps.csv', 'data/experiments')
df_abalone = pd.read_csv(p_abalone)
df_abalone2 = pd.read_csv(p_abalone2)
df_seismic = pd.read_csv(p_seismic)
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = split_data(df_abalone2)
#dataset = pd.read_csv('Social_Network_Ads.csv')
#X = dataset.iloc[:, [2, 3]].values
#y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test set
#from sklearn.cross_validation import train_test_split
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Decision Tree Classification to the Training set
