def spline(X, y, features, outcome):
# estimator = linear_model.Ridge(solver='svd')
estimator = Earth(feature_importance_type='rss')
feature_s = pd.Series(
data=features, index=['x' + str(i) for i in np.arange(len(features))])
table = []
estimator.fit(X, y)
# if estimator.rsq_ > 0.5:
# print outcome, 'r2 score: ', estimator.rsq_
print '================='
print 'Features Importance:'
# printing feature importances with x0 to xn
# mapped to real features name
for line in estimator.summary_feature_importances().split('\n'):
line_cleaned = line.split()
if len(line_cleaned) == 2:
print line_cleaned[0], line_cleaned[1], \
feature_s[line_cleaned[0]]
print 'r2 score: ', estimator.rsq_
return estimator
10 * X[:, 3] +
5 * X[:, 4] + numpy.random.uniform(size=m))
# Fit an Earth model
criteria = ('rss', 'gcv', 'nb_subsets')
model = Earth(max_degree=3,
max_terms=10,
minspan_alpha=.5,
feature_importance_type=criteria,
verbose=True)
model.fit(X, y)
rf = RandomForestRegressor()
rf.fit(X, y)
# Print the model
print(model.trace())
print(model.summary())
print(model.summary_feature_importances(sort_by='gcv'))
# Plot the feature importances
importances = model.feature_importances_
importances['random_forest'] = rf.feature_importances_
criteria = criteria + ('random_forest',)
idx = 1
fig = plt.figure(figsize=(20, 10))
labels = ['$x_{}$'.format(i) for i in range(n)]
for crit in criteria:
plt.subplot(2, 2, idx)
plt.bar(numpy.arange(len(labels)),
importances[crit],
align='center',
color='red')
def csc(df, hamming_string_dict, outdir, filename):
"""CRISPR Specificity Correction
:param df: pandas dataframe with first column as gRNA and second column as logFC/metric
:param hamming_string_dict: CSC onboard dictionary object with key as gRNA and value as Hamming metrics
:param outdir: absolute filepath to output directory
:param filename: name of input file to be used as part of output filename
:return: CSC adjustment
"""
# MARS compatible file
df_mars_lst = []
df_v = np.asarray(df)
for i in range(len(df_v)):
row_lst = []
grna, metric = df_v[i][0], df_v[i][1]
try:
metric = float(metric)
except ValueError:
sys.stdout.write(
'WARNING: encountered %s which is not float compatible, skipping\n'
% metric)
continue
row_lst.append(grna)
try:
for jj in hamming_string_dict[grna]:
row_lst.append(jj)
row_lst.append(metric)
df_mars_lst.append(row_lst)
except KeyError:
sys.stdout.write('\n%s not found in selected library: passing\n' %
grna)
continue
df = pd.DataFrame(df_mars_lst,
columns=[
'gRNA', 'specificity', 'h0', 'h1', 'h2', 'h3',
'original_value'
])
# exclude infinte specificity non-target gRNAs
df = df[df['h0'] != 0]
# isolate pertinent confounder variables
df_confounders = df[['specificity', 'h0', 'h1', 'h2', 'h3']]
# knots
knots = df['original_value'].quantile([0.25, 0.5, 0.75, 1])
# training and testing data
train_x, test_x, train_y, test_y = train_test_split(df_confounders,
df['original_value'],
test_size=0.10,
random_state=1)
# Fit an Earth model
model = Earth(feature_importance_type='gcv')
try:
model.fit(train_x, train_y)
except ValueError:
sys.stdout.write(
'\nValue Error encountered. Model unable to be trained. Exiting CSC Novo\n'
)
model_processed = 'F'
sys.stdout.write(
'training input x data\n %s\ntraining input y data\n %s\n' %
(train_x, train_y))
return model_processed
# Print the model
print(model.trace())
print(model.summary())
print(model.summary_feature_importances())
# Plot the model
y_hat = model.predict(test_x)
# calculating RMSE values
rms1 = sqrt(mean_squared_error(test_y, y_hat))
print('\n\nRMSE on Predictions\n\n')
print(rms1)
# calculating R^2 for training
print('\n\nR^2 on Training Data\n\n')
print(model.score(train_x, train_y))
# calculating R^2 for testing
print('\n\nR^2 on Testing Data\n\n')
print(model.score(test_x, test_y))
# write out model metrics
with open('%s/csc_model_metrics_%s.txt' % (outdir, filename),
'w') as outfile:
outfile.write('%s\n%s\n%s\nRMSE on Predictions\n%s' %
(model.trace(), model.summary(),
model.summary_feature_importances(), rms1))
if rms1 <= 1.0:
#model processed
model_processed = 'T'
# full data prediction
df['earth_adjustment'] = model.predict(df_confounders)
# CSC correction
df['earth_corrected'] = df['original_value'] - df['earth_adjustment']
# main write out
df.to_csv('%s/csc_output_%s_earth_patched.csv' % (outdir, filename))
# pickle write out
model_file = open(
'%s/csc_output_%s_earth_model.pl' % (outdir, filename), 'wb')
pl.dump(model, model_file)
model_file.close()
sys.stdout.write('\nCSC adjustment complete\n')
sys.stdout.write('\nCSC output files written to %s\n' % outdir)
return model_processed
else:
sys.stdout.write(
'\nCSC adjustment not computed as model residual mean squared error exceeds 1.0\n'
)
model_processed = 'F'
return model_processed
data = df[start_p:stop_p]
data['Day'] = data.index.dayofyear #add day
data = data.interpolate(limit=300000000,limit_direction='both').astype('float32') #interpolate neighbor first, for rest NA fill with mean()
conclude_df=pd.DataFrame()
for n_out in range(1,n_future+1):
X,y,xlabels = to_supervise(data,target,n_out)
criteria = ('rss', 'gcv', 'nb_subsets')
model = Earth(enable_pruning = True,
# max_degree=3,
# max_terms=20,
minspan_alpha=.5,
feature_importance_type=criteria,
verbose=True)
model.fit(X,y,xlabels=xlabels)
nbsub = model.summary_feature_importances(sort_by='nb_subsets')[:2000].split()[3:83]
gcv = model.summary_feature_importances(sort_by='gcv')[:2000].split()[3:83]
rss = model.summary_feature_importances(sort_by='rss')[:2000].split()[3:83]
rss,gcv,nbsub = toDF(rss),toDF(gcv),toDF(nbsub)
top20=pd.concat([rss,gcv,nbsub],ignore_index=True)
top20 = pd.concat([rss,gcv,nbsub],ignore_index=True).drop_duplicates('feature')
top20['timestep'] = n_out
#ADDED combine all result
conclude_df = pd.concat([conclude_df,top20],ignore_index=True)
if mode=='day':
conclude_df.drop_duplicates('feature').to_csv('/home/song/Public/Song/Work/Thesis/MAR/[CPY012]featurelist_MAR_daily.csv',index=False)
conclude_df.to_csv('/home/song/Public/Song/Work/Thesis/MAR/[CPY012]featurelist_MAR_daily_Fullreport.csv',index=False)
elif mode=='hour':
