def add_filters(self, filter_list):
if filter_list is None:
return
for (feature, operator, threshold) in filter_list:
fio = FeatureIO(self.data)
self.data = fio.custom_feature_filter(feature, operator, threshold)
self.set_up_data_from_features()
return
def csv_add_features(self, csvsrc, csvdest):
afm_dict=dict()
param_dict=dict()
#E900 column
e900_dict = dict()
for elem in ['P','Ni','Cu','Mn']: #Si, C not used in e900
e900_dict['wt%s' % elem] = 'wt_percent_%s' % elem
e900_dict['fluencestr'] = 'fluence_n_cm2'
e900_dict['tempC'] = 'temperature_C'
e900_dict['prod_ID'] = 'product_id'
afm_dict['DBTT.E900'] = dict(e900_dict)
param_dict['DBTT.E900'] = dict()
#get_dataframe
csv_dataparser = DataParser()
csv_dataframe = csv_dataparser.import_data("%s.csv" % os.path.join(self.save_path, csvsrc))
#add features
for afm in afm_dict.keys():
(feature_name, feature_data) = cf_help.get_custom_feature_data(class_method_str = afm,
starting_dataframe = csv_dataframe,
param_dict = dict(param_dict[afm]),
addl_feature_method_kwargs = dict(afm_dict[afm]))
fio = FeatureIO(csv_dataframe)
csv_dataframe = fio.add_custom_features([feature_name],feature_data)
#add log10 features
log10_dict=dict()
log10_dict['fluence_n_cm2'] = dict()
log10_dict['flux_n_cm2_sec'] = dict()
for lkey in log10_dict.keys():
orig_data = csv_dataframe[lkey]
log10_data = np.log10(orig_data)
fio = FeatureIO(csv_dataframe)
csv_dataframe = fio.add_custom_features(["log(%s)" % lkey], log10_data)
#add normalizations
norm_dict = dict()
norm_dict['log(fluence_n_cm2)']=dict()
norm_dict['log(fluence_n_cm2)']['smin'] = 17
norm_dict['log(fluence_n_cm2)']['smax'] = 25
norm_dict['log(flux_n_cm2_sec)']=dict()
norm_dict['log(flux_n_cm2_sec)']['smin'] = 10
norm_dict['log(flux_n_cm2_sec)']['smax'] = 15
norm_dict['temperature_C']=dict()
norm_dict['temperature_C']['smin'] = 270
norm_dict['temperature_C']['smax'] = 320
for elem in ["P","C","Cu","Ni","Mn","Si"]:
norm_dict["at_percent_%s" % elem] = dict()
norm_dict["at_percent_%s" % elem]['smin'] = 0.0
norm_dict["at_percent_%s" % elem]['smax'] = 1.717 #max Mn atomic percent
for nkey in norm_dict.keys():
fnorm = FeatureNormalization(csv_dataframe)
scaled_feature = fnorm.minmax_scale_single_feature(nkey,
smin=norm_dict[nkey]['smin'],
smax=norm_dict[nkey]['smax'])
fio = FeatureIO(csv_dataframe)
csv_dataframe = fio.add_custom_features(["N(%s)" % nkey],scaled_feature)
csv_dataframe.to_csv("%s.csv" % os.path.join(self.save_path, csvdest))
return
def get_afm_updated_dataset(self, indiv_df, indiv_params):
"""Update dataframe with additional feature methods
"""
for afm in indiv_params.keys():
if afm == 'model': #model dealt with separately
continue
afm_kwargs = dict(indiv_params[afm])
(feature_name, feature_data) = cf_help.get_custom_feature_data(
afm,
starting_dataframe=indiv_df,
addl_feature_method_kwargs=dict(afm_kwargs))
fio = FeatureIO(indiv_df)
indiv_df = fio.add_custom_features([afm], feature_data)
return indiv_df
def subtraction(self, col1="", col2="", num1="", num2="", **params):
"""Testing function.
col1 <str>: first feature name
col2 <str>: second feature name
num1 <float>: number to multiply col1 by
num2 <float>: number to subtract
"""
col1_data = self.df[col1]
col2_data = self.df[col2]
new_data = (col1_data * num1) - col2_data + num2
fio = FeatureIO(self.df)
new_df = fio.add_custom_features(["Subtraction_test"],new_data)
fnorm = FeatureNormalization(new_df)
N_new_data = fnorm.minmax_scale_single_feature("Subtraction_test")
return N_new_data
def test_custom_feature_filter(self):
df = FeatureIO(dataframe=self.df1).custom_feature_filter(
feature='AtomicNumber_composition_average',
operator='<',
threshold=21)
self.assertTrue(df.shape[0] < self.df1.shape[0])
return
def test_add_custom_features(self):
df1columns = self.df1.shape[1]
df = FeatureIO(dataframe=self.df1).add_custom_features(
features_to_add=['test_feature'],
data_to_add=np.zeros(shape=[
self.df1.shape[0],
]))
self.assertTrue(df1columns < df.shape[1])
return
def add_normalization(self, cname, verbose=0):
df = self.dfs[cname]
norm_dict = dict()
norm_dict['log(fluence_n_cm2)'] = dict()
norm_dict['log(fluence_n_cm2)']['smin'] = 17
norm_dict['log(fluence_n_cm2)']['smax'] = 25
norm_dict['log(flux_n_cm2_sec)'] = dict()
norm_dict['log(flux_n_cm2_sec)']['smin'] = 10
norm_dict['log(flux_n_cm2_sec)']['smax'] = 15
norm_dict['log(eff fl 100p=10)'] = dict()
norm_dict['log(eff fl 100p=10)']['smin'] = 17
norm_dict['log(eff fl 100p=10)']['smax'] = 25
norm_dict['log(eff fl 100p=20)'] = dict()
norm_dict['log(eff fl 100p=20)']['smin'] = 17
norm_dict['log(eff fl 100p=20)']['smax'] = 25
norm_dict['log(eff fl 100p=26)'] = dict()
norm_dict['log(eff fl 100p=26)']['smin'] = 17
norm_dict['log(eff fl 100p=26)']['smax'] = 25
norm_dict['log(eff fl 100p=23)'] = dict()
norm_dict['log(eff fl 100p=23)']['smin'] = 17
norm_dict['log(eff fl 100p=23)']['smax'] = 25
norm_dict['log(eff fl 100p=8)'] = dict()
norm_dict['log(eff fl 100p=8)']['smin'] = 17
norm_dict['log(eff fl 100p=8)']['smax'] = 25
norm_dict['temperature_C'] = dict()
norm_dict['temperature_C']['smin'] = 270
norm_dict['temperature_C']['smax'] = 320
for elem in ["P", "C", "Cu", "Ni", "Mn", "Si"]:
norm_dict["at_percent_%s" % elem] = dict()
norm_dict["at_percent_%s" % elem]['smin'] = 0.0
norm_dict["at_percent_%s" %
elem]['smax'] = 1.717 #max Mn atomic percent
for nkey in norm_dict.keys():
fnorm = FeatureNormalization(df)
scaled_feature = fnorm.minmax_scale_single_feature(
nkey,
smin=norm_dict[nkey]['smin'],
smax=norm_dict[nkey]['smax'])
fio = FeatureIO(df)
df = fio.add_custom_features(["N(%s)" % nkey], scaled_feature)
self.dfs[cname] = df
return
def test_remove_duplicate_columns(self):
# Manually add duplicate column to dataframe
self.df1['AtomicNumber_composition_average_copy'] = pd.Series(
self.df1['AtomicNumber_composition_average'], index=self.df1.index)
self.df1.rename(columns={
'AtomicNumber_composition_average_copy':
'AtomicNumber_composition_average'
},
inplace=True)
df = FeatureIO(dataframe=self.df1).remove_duplicate_columns()
self.assertTrue(df.shape[1] < self.df1.shape[1])
return
def calculate_EffectiveFluence(self,
pvalue=0,
ref_flux=3e10,
flux_feature="",
fluence_feature="",
scale_min=1e17,
scale_max=1e25,
**params):
"""Calculate effective fluence
"""
fluence = self.df[fluence_feature]
flux = self.df[flux_feature]
EFl = fluence * (ref_flux / flux)**pvalue
EFl = np.log10(EFl)
fio = FeatureIO(self.df)
new_df = fio.add_custom_features(["EFl"], EFl)
fnorm = FeatureNormalization(new_df)
N_EFl = fnorm.minmax_scale_single_feature("EFl",
smin=np.log10(scale_min),
smax=np.log10(scale_max))
return N_EFl
def make_data(self):
n_samples, n_features = 100, 5
y = self.random_state.randn(n_samples)
X = self.random_state.randn(n_samples, n_features)
nidx = np.arange(0, n_samples)
self.dataframe = pd.DataFrame(index=nidx)
num_cat = self.random_state.randint(0, 4, n_samples)
cats = ['A', 'B', 'C', 'D']
str_cat = [cats[nc] for nc in num_cat]
time = nidx * np.pi / 8.0
sine_feature = np.sin(time) + X[:, 0] #add noise
linear_feature = 100 * time + 30.0 + X[:, 1] #add noise
y_feature = np.sin(time) + y / 10.0
y_feature_error = X[:, 3] / X[:, 4] / 100.0 #add random error
d_cols = dict()
d_cols["num_idx"] = nidx
d_cols["num_cat"] = num_cat
d_cols["str_cat"] = str_cat
d_cols["time"] = time
d_cols["sine_feature"] = sine_feature
d_cols["linear_feature"] = linear_feature
d_cols["y_feature"] = y_feature
d_cols["y_feature_error"] = y_feature_error
cols = list(d_cols.keys())
cols.sort()
for col in cols:
fio = FeatureIO(self.dataframe)
self.dataframe = fio.add_custom_features([col], d_cols[col])
fnorm = FeatureNormalization(self.dataframe)
N_sine_feature = fnorm.minmax_scale_single_feature("sine_feature")
N_linear_feature = fnorm.minmax_scale_single_feature("linear_feature")
fio = FeatureIO(self.dataframe)
self.dataframe = fio.add_custom_features(["N_sine_feature"],
N_sine_feature)
fio = FeatureIO(self.dataframe)
self.dataframe = fio.add_custom_features(["N_linear_feature"],
N_sine_feature)
return
def test_keep_custom_features(self):
df = FeatureIO(dataframe=self.df1).keep_custom_features(
features_to_keep=['AtomicNumber_composition_average'])
self.assertTrue(
['AtomicNumber_composition_average'] == df.columns.values.tolist())
return
def test_remove_custom_features(self):
df = FeatureIO(dataframe=self.df1).remove_custom_features(
features_to_remove=['AtomicNumber_composition_average'])
self.assertTrue('AtomicNumber_composition_average' not in
df.columns.values.tolist())
return
def add_feature(self, feature_name, feature_data):
fio = FeatureIO(self.data)
self.data = fio.add_custom_features([feature_name], feature_data)
return
def add_prediction_sigma(self, prediction_data_sigma):
fio = FeatureIO(self.data)
self.data = fio.add_custom_features(["Prediction Sigma"],
prediction_data_sigma)
self.target_prediction_sigma = self.data["Prediction Sigma"]
return
def add_prediction(self, prediction_data):
fio = FeatureIO(self.data)
self.data = fio.add_custom_features(["Prediction"], prediction_data)
self.target_prediction = self.data["Prediction"]
return
def _create_data_dict(self):
data_dict = dict()
for data_name in self.data_setup.keys():
data_path = self.configdict['Data Setup'][data_name]['data_path']
logging.info(
'Creating data dict for data path %s and data name %s' %
(data_path, data_name))
data_weights = self.data_setup[data_name]['weights']
if 'labeling_features' in self.general_setup.keys():
labeling_features = self._string_or_list_input_to_list(
self.general_setup['labeling_features'])
else:
labeling_features = None
if 'target_error_feature' in self.general_setup.keys():
target_error_feature = self.general_setup[
'target_error_feature']
else:
target_error_feature = None
if 'grouping_feature' in self.general_setup.keys():
grouping_feature = self.general_setup['grouping_feature']
else:
grouping_feature = None
if 'Feature Generation' in self.configdict.keys():
if self.configdict['Feature Generation']['perform_feature_generation'] == bool(True) or \
self.configdict['Feature Generation']['perform_feature_generation'] == "True":
generate_features = True
else:
generate_features = False
else:
generate_features = False
if 'Feature Normalization' in self.configdict.keys():
if self.configdict['Feature Normalization']['normalize_x_features'] == bool(True) or \
self.configdict['Feature Normalization']['normalize_x_features'] == "True":
normalize_x_features = True
else:
normalize_x_features = False
if self.configdict['Feature Normalization']['normalize_y_feature'] == bool(True) or \
self.configdict['Feature Normalization']['normalize_y_feature'] == "True":
normalize_y_feature = True
else:
normalize_y_feature = False
else:
normalize_x_features = False
normalize_y_feature = False
if 'Feature Selection' in self.configdict.keys():
if self.configdict['Feature Selection']['perform_feature_selection'] == bool(True) or \
self.configdict['Feature Selection']['perform_feature_selection'] == "True":
select_features = True
else:
select_features = False
else:
select_features = False
logging.info("Feature Generation: %s" % generate_features)
logging.info("Feature Normalization (x_features): %s" %
normalize_x_features)
logging.info("Feature Normalization (y_feature): %s" %
normalize_y_feature)
logging.info("Feature Selection: %s" % select_features)
# Parse input data file
Xdata, ydata, x_features, y_feature, dataframe = self._parse_input_data(
data_path)
# Plot initial histogram of input target data
DataframeUtilities().plot_dataframe_histogram(
configdict=self.configdict,
dataframe=dataframe,
y_feature=y_feature)
original_x_features = list(x_features)
original_columns = list(dataframe.columns)
logging.debug("original columns: %s" % original_columns)
# Remove any missing rows from dataframe
#dataframe = dataframe.dropna()
# Save off label and grouping data
dataframe_labeled = pd.DataFrame()
dataframe_grouped = pd.DataFrame()
if not (labeling_features is None):
dataframe_labeled = FeatureIO(
dataframe=dataframe).keep_custom_features(
features_to_keep=labeling_features,
y_feature=y_feature)
if normalize_x_features == bool(True):
dataframe_labeled, scaler = FeatureNormalization(
dataframe=dataframe_labeled,
configdict=self.configdict).normalize_features(
x_features=labeling_features, y_feature=y_feature)
if not (grouping_feature is None):
dataframe_grouped = FeatureIO(
dataframe=dataframe).keep_custom_features(
features_to_keep=[grouping_feature],
y_feature=y_feature)
# Generate additional descriptors, as specified in input file (optional)
if generate_features:
dataframe = self._perform_feature_generation(
dataframe=dataframe)
# Actually, the x_features_NOUSE is required if starting from no features and doing feature generation. Not renaming for now. RJ 7/17
Xdata, ydata, x_features_NOUSE, y_feature, dataframe = DataParser(
configdict=self.configdict).parse_fromdataframe(
dataframe=dataframe, target_feature=y_feature)
else:
Xdata, ydata, x_features, y_feature, dataframe = DataParser(
configdict=self.configdict).parse_fromdataframe(
dataframe=dataframe, target_feature=y_feature)
# First remove features containing strings before doing feature normalization or other operations, but don't remove grouping features
if generate_features == bool(True):
nonstring_x_features, dataframe_nostrings = MiscFeatureOperations(
configdict=self.configdict
).remove_features_containing_strings(
dataframe=dataframe, x_features=x_features_NOUSE)
#Remove columns containing all entries of NaN
dataframe_nostrings = dataframe_nostrings.dropna(axis=1,
how='all')
# Re-obtain x_feature list as some features may have been dropped
Xdata, ydata, x_features_NOUSE, y_feature, dataframe_nostrings = DataParser(
configdict=self.configdict).parse_fromdataframe(
dataframe=dataframe_nostrings,
target_feature=y_feature)
else:
nonstring_x_features, dataframe_nostrings = MiscFeatureOperations(
configdict=self.configdict
).remove_features_containing_strings(dataframe=dataframe,
x_features=x_features)
# Remove columns containing all entries of NaN
dataframe_nostrings = dataframe_nostrings.dropna(axis=1, how='all')
# Fill spots with NaN to be empty string
dataframe_nostrings = dataframe_nostrings.dropna(axis=1, how='any')
# Re-obtain x_feature list as some features may have been dropped
Xdata, ydata, x_features, y_feature, dataframe_nostrings = DataParser(
configdict=self.configdict).parse_fromdataframe(
dataframe=dataframe_nostrings, target_feature=y_feature)
logging.debug("pre-changes:%s" % dataframe_nostrings.columns)
# Normalize features (optional)
if normalize_x_features == bool(
True) or normalize_y_feature == bool(True):
fn = FeatureNormalization(dataframe=dataframe_nostrings,
configdict=self.configdict)
dataframe_nostrings, scaler = fn.normalize_features(
x_features=x_features,
y_feature=y_feature,
normalize_x_features=normalize_x_features,
normalize_y_feature=normalize_y_feature)
x_features, y_feature = DataParser(
configdict=self.configdict).get_features(
dataframe=dataframe_nostrings,
target_feature=y_feature)
# Perform feature selection and dimensional reduction, as specified in the input file (optional)
if (select_features
== bool(True)) and (y_feature
in dataframe_nostrings.columns):
# Remove any additional columns that are not x_features using to be fit to data
features = dataframe_nostrings.columns.values.tolist()
features_to_remove = []
for feature in features:
if feature not in x_features and feature not in y_feature:
features_to_remove.append(feature)
dataframe_nostrings = FeatureIO(
dataframe=dataframe_nostrings).remove_custom_features(
features_to_remove=features_to_remove)
dataframe_nostrings = self._perform_feature_selection(
dataframe=dataframe_nostrings,
x_features=x_features,
y_feature=y_feature)
x_features, y_feature = DataParser(
configdict=self.configdict).get_features(
dataframe=dataframe_nostrings,
target_feature=y_feature)
logging.debug("post-removal:%s" % dataframe_nostrings.columns)
# Combine the input dataframe, which has undergone feature generation and normalization, with the grouped and labeled features of original dataframe
# First, need to generate dataframe that only has the grouped and labeled features
grouping_and_labeling_features = []
duplicate_features = []
if 'grouping_feature' in self.configdict['General Setup'].keys():
grouping_and_labeling_features.append(grouping_feature)
if 'labeling_features' in self.configdict['General Setup'].keys():
for feature in labeling_features:
grouping_and_labeling_features.append(feature)
if feature in x_features:
if feature not in duplicate_features:
duplicate_features.append(feature)
# Now merge dataframes
dataframe_labeled_grouped = DataframeUtilities(
).merge_dataframe_columns(dataframe1=dataframe_labeled,
dataframe2=dataframe_grouped)
dataframe_merged = DataframeUtilities().merge_dataframe_columns(
dataframe1=dataframe_nostrings,
dataframe2=dataframe_labeled_grouped)
#Add string columns back in
string_x_features = list()
for my_x_feature in x_features:
if my_x_feature in nonstring_x_features:
pass
else:
string_x_features.append(my_x_feature)
logging.debug("string features: %s" % string_x_features)
for string_x_feature in string_x_features:
dataframe_merged[string_x_feature] = dataframe_orig_dropped_na[
string_x_feature]
# Need to remove duplicate features after merging.
logging.debug("merged:%s" % dataframe_merged.columns)
dataframe_rem = FeatureIO(
dataframe=dataframe_merged).remove_duplicate_columns()
myXdata, myydata, myx_features, myy_feature, dataframe_final = DataParser(
configdict=self.configdict).parse_fromdataframe(
dataframe=dataframe_rem, target_feature=y_feature)
combined_x_features = list()
logging.debug("total features:%s" % myx_features)
for feature in myx_features:
if (feature in original_x_features) or not (
feature in original_columns
): #originally designated, or created from feature generation
combined_x_features.append(feature)
logging.debug("combined x features:%s" % combined_x_features)
data_dict[data_name] = DataHandler(
data=dataframe_final,
input_data=dataframe_final[combined_x_features],
target_data=myydata,
input_features=combined_x_features,
target_feature=myy_feature,
target_error_feature=target_error_feature,
labeling_features=labeling_features,
grouping_feature=grouping_feature) #
logging.info('Parsed the input data located under %s' % data_path)
# Get dataframe stats
DataframeUtilities.save_all_dataframe_statistics(
dataframe=dataframe_final, configdict=self.configdict)
return data_dict, y_feature
