Exemple #1
0
def run_csv(fin, uid_feature, label_feature, clfs=DBG_std_clfs):
    """ Turn a CSV into an Experiment then turn the Experiment into models"""

    sa = open_csv_as_sa(fin)
    labels = sa[label_feature]
    M = remove_cols(sa, label_feature)
    exp = Experiment(M, labels, clfs=clfs)
    register_exp(exp, uid_feature)
Exemple #2
0
def run_csv(fin, uid_feature, label_feature):
    sa = open_csv_as_sa(fin)
    labels = sa[label_feature]
    M = remove_cols(sa, label_feature)
    exp = Experiment(M, labels, clfs=DBG_std_clfs)
    exp.run()
    last_experiments[current_user.id] = exp
    clear_models(current_user.id)
    for trial in exp.trials:
        for subset in trial.runs:
            for run in subset:
                register_model(current_user.id, run.clf, dt.now(),
                               run.M[run.train_indices],
                               run.M[run.test_indices],
                               run.labels[run.train_indices],
                               run.labels[run.test_indices], run.col_names,
                               uid_feature)
Exemple #3
0
def run_csv(fin, uid_feature, label_feature):
    sa = open_csv_as_sa(fin)
    labels = sa[label_feature]
    M = remove_cols(sa, label_feature)
    exp = Experiment(M, labels, clfs=DBG_std_clfs)
    exp.run()
    last_experiments[current_user.id] = exp
    clear_models(current_user.id)
    for trial in exp.trials:
        for subset in trial.runs:
            for run in subset:
                register_model(
                        current_user.id,
                        run.clf, 
                        dt.now(),
                        run.M[run.train_indices], 
                        run.M[run.test_indices], 
                        run.labels[run.train_indices], 
                        run.labels[run.test_indices], 
                        run.col_names, 
                        uid_feature)
Exemple #4
0
                               plot_kernel_density,
                               plot_box_plot)

from diogenes.grid_search import Experiment 
from diogenes.grid_search import std_clfs as std_clfs
from diogenes.utils import remove_cols


data = open_csv_url(
            'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv',  
            delimiter=';')
y = data['quality']
M = remove_cols(data, 'quality')

y = y < np.average(y)


if False:
    for x in describe_cols(M):
        print x

if False:
   plot_correlation_scatter_plot(M) 
   plot_correlation_matrix(M)
   plot_kernel_density(M['f0']) #no designation of col name
   plot_box_plot(M['f0']) #no designation of col name

exp = Experiment(M, y, clfs=std_clfs)
exp.make_csv()

Exemple #5
0
from diogenes.read import open_csv_url
from diogenes.display import (plot_correlation_scatter_plot,
                              plot_correlation_matrix, plot_kernel_density,
                              plot_box_plot)

from diogenes.grid_search import Experiment
from diogenes.grid_search import std_clfs as std_clfs
from diogenes.utils import remove_cols

data = open_csv_url(
    'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv',
    delimiter=';')
y = data['quality']
M = remove_cols(data, 'quality')

y = y < np.average(y)

if False:
    for x in describe_cols(M):
        print x

if False:
    plot_correlation_scatter_plot(M)
    plot_correlation_matrix(M)
    plot_kernel_density(M['f0'])  #no designation of col name
    plot_box_plot(M['f0'])  #no designation of col name

exp = Experiment(M, y, clfs=std_clfs)
exp.make_csv()