def test_miner_init_smoke(miner_df):
logcf = lambda row, x: mvn.logpdf(x, np.zeros(2), np.eye(len(x)))
miner = MInER(miner_df, logcf, ['x_2', 'x_3'], n_models=2, use_mp=False)
miner.init_models()
assert hasattr(miner, '_logcf')
assert hasattr(miner, '_miner_cols')
assert hasattr(miner, '_miner_col_idxs')
assert not hasattr(miner, 'combat_wombat')
def test_fit_smoke(miner_df):
logcf = lambda row, x: mvn.logpdf(x, np.zeros(2), np.eye(len(x)))
miner = MInER(miner_df, logcf, ['x_2', 'x_3'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(1, 5)
assert(not np.any(np.isnan(miner._df['x_2'].values)))
assert(not np.any(np.isnan(miner._df['x_3'].values)))
def test_miner_init_smoke(miner_df):
logcf = lambda row, x: mvn.logpdf(x, np.zeros(2), np.eye(len(x)))
miner = MInER(miner_df, logcf, ['x_2', 'x_3'], n_models=2, use_mp=False)
miner.init_models()
assert hasattr(miner, '_logcf')
assert hasattr(miner, '_miner_cols')
assert hasattr(miner, '_miner_col_idxs')
assert not hasattr(miner, 'combat_wombat')
def test_convert_uniform_column_to_normal(miner_df):
logcf = lambda row, x: norm.logpdf(x[0], 0, 1)
miner = MInER(miner_df, logcf, ['x_2'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(20, 10)
assert(not np.any(np.isnan(miner._df['x_2'].values)))
assert(not np.any(np.isnan(miner._df['x_3'].values)))
assert(normaltest(miner._df['x_2'])[1] > .05)
assert(normaltest(miner._df['x_3'])[1] < .05)
def test_fit_changes_data_sometimes_one_col(miner_df):
df = copy.deepcopy(miner_df)
logcf = lambda row, x: 0.0
miner = MInER(miner_df, logcf, ['x_2'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(1, 5)
assert(not np.any(np.isnan(miner._df['x_2'].values)))
assert(not np.any(np.isnan(miner._df['x_3'].values)))
for i in range(miner._n_cols):
assert(miner._df['x_1'].ix[i] == df['x_1'].ix[i])
assert(miner._df['x_2'].ix[i] != df['x_2'].ix[i])
assert(miner._df['x_3'].ix[i] == df['x_3'].ix[i])
def test_miner_changes_columns_differently(miner_df):
df = copy.deepcopy(miner_df)
logcf = lambda row, x: 0.0
x_02 = df.loc[0, 'x_2']
x_03 = df.loc[0, 'x_3']
miner = MInER(miner_df, logcf, ['x_2', 'x_3'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(1, 5)
assert miner._df.loc[0, 'x_2'] != x_02
assert miner._df.loc[0, 'x_3'] != x_03
for i in range(df.shape[0]):
assert miner._df.loc[i, 'x_2'] != miner._df.loc[i, 'x_3']
def test_fit_smoke(miner_df):
logcf = lambda row, x: mvn.logpdf(x, np.zeros(2), np.eye(len(x)))
miner = MInER(miner_df, logcf, ['x_2', 'x_3'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(1, 5)
assert (not np.any(np.isnan(miner._df['x_2'].values)))
assert (not np.any(np.isnan(miner._df['x_3'].values)))
def test_convert_uniform_column_to_normal(miner_df):
logcf = lambda row, x: norm.logpdf(x[0], 0, 1)
miner = MInER(miner_df, logcf, ['x_2'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(20, 10)
assert (not np.any(np.isnan(miner._df['x_2'].values)))
assert (not np.any(np.isnan(miner._df['x_3'].values)))
assert (normaltest(miner._df['x_2'])[1] > .05)
assert (normaltest(miner._df['x_3'])[1] < .05)
def test_fit_changes_data_sometimes_one_col(miner_df):
df = copy.deepcopy(miner_df)
logcf = lambda row, x: 0.0
miner = MInER(miner_df, logcf, ['x_2'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(1, 5)
assert (not np.any(np.isnan(miner._df['x_2'].values)))
assert (not np.any(np.isnan(miner._df['x_3'].values)))
for i in range(miner._n_cols):
assert (miner._df['x_1'].ix[i] == df['x_1'].ix[i])
assert (miner._df['x_2'].ix[i] != df['x_2'].ix[i])
assert (miner._df['x_3'].ix[i] == df['x_3'].ix[i])
def test_miner_changes_columns_differently(miner_df):
df = copy.deepcopy(miner_df)
logcf = lambda row, x: 0.0
x_02 = df.loc[0, 'x_2']
x_03 = df.loc[0, 'x_3']
miner = MInER(miner_df, logcf, ['x_2', 'x_3'], n_models=2, use_mp=False)
miner.init_models()
miner.fit(1, 5)
assert miner._df.loc[0, 'x_2'] != x_02
assert miner._df.loc[0, 'x_3'] != x_03
for i in range(df.shape[0]):
assert miner._df.loc[i, 'x_2'] != miner._df.loc[i, 'x_3']