def plot(val_fn, pts_fn, output_fn):
points = []
with open(pts_fn) as fp:
for line in fp.xreadlines():
points.append(map(float, line.split()))
values = []
with open(val_fn) as fp:
for line in fp.xreadlines():
values.append(float(line.split()[1]))
xx = [pt[0] for pt in points]
yy = [pt[1] for pt in points]
print "X:", min(xx), max(xx)
print "Y:", min(yy), max(yy)
m = min(values)
values = [(v - m) % 1. for v in values]
print "V:", min(values), max(values)
# hsv()
myData = numpy.array(points)
#results = PCA(myData,2)
pca = PCA(n_components=2)
results = pca.fit_transform(points)
fig = figure()
scatter(results[:, 0], results[:, 1], s=10, c=values, cmap="spectral")
colorbar()
# ax = fig.add_axes([-.05,-.1,1.1,1.1])
ax = axes()
ax.set_axis_off()
ax.set_aspect('equal', 'box')
# adjust(0,0,1,1,0,0)
fig.savefig(output_fn)
avg_sulphates = df.sulphates / np.mean(df.sulphates)
avg_alcohol = df.alcohol / np.mean(df.alcohol)
#normalized dataset using the max of column method
max_df = pd.DataFrame({'fixed_acidity': max_fixed_acidity, 'volatile_acidity': max_volatile_acidity, 'citric_acid':max_citric_acid, \
'residual_sugar': max_residual_sugar, 'chlorides': max_chlorides, 'free_sulfur_dioxide': max_free_sulfur_dioxide, \
'total_sulfur_dioxide': max_total_sulfur_dioxide, 'density': max_density, 'pH': max_pH, 'sulphates': max_sulphates, 'alcohol':max_alcohol})
#normalized datset using the average of column method
avg_df = pd.DataFrame({'fixed_acidity': avg_fixed_acidity, 'volatile_acidity': avg_volatile_acidity, 'citric_acid':avg_citric_acid, \
'residual_sugar': avg_residual_sugar, 'chlorides': avg_chlorides, 'free_sulfur_dioxide': avg_free_sulfur_dioxide, \
'total_sulfur_dioxide': avg_total_sulfur_dioxide, 'density': avg_density, 'pH': avg_pH, 'sulphates': avg_sulphates, 'alcohol':avg_alcohol})
#principal component analysis of all three data frames
pca = PCA()
pca_df = pd.DataFrame(pca.fit_transform(df), columns=column_names)
pca_avg_df = pd.DataFrame(pca.fit_transform(avg_df), columns=column_names)
pca_max_df = pd.DataFrame(pca.fit_transform(max_df), columns=column_names)
######################### VIZ - SCREE CHARTS & SCATTER ###########################
PALETTE = [
"#e6194b", "#3cb44b", "#ffe119", "#0082c8", "#f58231", "#911eb4",
"#46f0f0", "#f032e6", "#d2f53c", "#008080", "#e6beff"
]
if option == 1:
plt.hist(quality, bins=10, color=PALETTE[7])
plt.show()
def featurize(df, meta):
df['flux_ratio_sq'] = np.power(df['flux'] / df['flux_err'], 2.0)
df['flux_by_flux_ratio_sq'] = df['flux'] * df['flux_ratio_sq']
# train[detected==1, mjd_diff:=max(mjd)-min(mjd), by=object_id]
aggs = {
'flux': ['min', 'max', 'mean', 'median', 'std', 'skew'],
'flux_err': ['min', 'max', 'mean', 'median', 'std', 'skew'],
'detected': ['mean'],
'flux_ratio_sq': ['sum', 'skew'],
'flux_by_flux_ratio_sq': ['sum', 'skew']
}
# Features to compute with tsfresh library. Fft coefficient is meant to capture periodicity
fcp = {
'flux': {
'longest_strike_above_mean': None,
'longest_strike_below_mean': None,
'mean_change': None,
'mean_abs_change': None,
'length': None,
},
'flux_by_flux_ratio_sq': {
'longest_strike_above_mean': None,
'longest_strike_below_mean': None,
},
'flux_passband': {
'fft_coefficient': [{
'coeff': 0,
'attr': 'abs'
}, {
'coeff': 1,
'attr': 'abs'
}],
'kurtosis':
None,
'skewness':
None,
},
'mjd': {
'maximum': None,
'minimum': None,
'mean_change': None,
'mean_abs_change': None,
},
}
agg_df = df.groupby(['object_id']).agg(aggs)
new_columns = [k + '_' + agg for k in aggs.keys() for agg in aggs[k]]
agg_df.columns = new_columns
agg_df = agg_df.reset_index()
agg_df['flux_diff'] = agg_df['flux_max'] - agg_df['flux_min']
agg_df['flux_dif2'] = (agg_df['flux_max'] -
agg_df['flux_min']) / agg_df['flux_mean']
agg_df['flux_w_mean'] = agg_df['flux_by_flux_ratio_sq_sum'] / agg_df[
'flux_ratio_sq_sum']
agg_df['flux_dif3'] = (agg_df['flux_max'] -
agg_df['flux_min']) / agg_df['flux_w_mean']
# Added DWT features (ignore warnings from pywt.wavedec):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
wavelet_df = wavelet(df)
agg_df = agg_df.merge(right=wavelet_df, how='left', on='object_id')
print('MERGED WAVELET DF')
# Run PCA
X = agg_df.iloc[:, 22:]
X_std = StandardScaler().fit_transform(X)
pca = PCA(n_components=200)
pca_results = pd.DataFrame(pca.fit_transform(X_std))
pca_results.columns = ['PCA' + str(x + 1) for x in range(200)]
agg_df = agg_df.iloc[:, :22]
agg_df = pd.concat([agg_df, pca_results], axis=1)
# Add more tsfresh features with passband, flux, flux_ratio_sq:
agg_df_ts_flux_passband = extract_features(
df,
column_id='object_id',
column_sort='mjd',
column_kind='passband',
column_value='flux',
default_fc_parameters=fcp['flux_passband'],
n_jobs=4)
agg_df_ts_flux = extract_features(df,
column_id='object_id',
column_value='flux',
default_fc_parameters=fcp['flux'],
n_jobs=4)
agg_df_ts_flux_by_flux_ratio_sq = extract_features(
df,
column_id='object_id',
column_value='flux_by_flux_ratio_sq',
default_fc_parameters=fcp['flux_by_flux_ratio_sq'],
n_jobs=4)
# Add smart feature that is suggested here https://www.kaggle.com/c/PLAsTiCC-2018/discussion/69696#410538
# dt[detected==1, mjd_diff:=max(mjd)-min(mjd), by=object_id]
df_det = df[df['detected'] == 1].copy()
agg_df_mjd = extract_features(df_det,
column_id='object_id',
column_value='mjd',
default_fc_parameters=fcp['mjd'],
n_jobs=4)
agg_df_mjd['mjd_diff_det'] = agg_df_mjd['mjd__maximum'] - agg_df_mjd[
'mjd__minimum']
del agg_df_mjd['mjd__maximum'], agg_df_mjd['mjd__minimum']
agg_df_ts_flux_passband = agg_df_ts_flux_passband.reset_index()
agg_df_ts_flux_passband.rename(columns={'id': 'object_id'}, inplace=True)
agg_df_ts_flux = agg_df_ts_flux.reset_index()
agg_df_ts_flux.rename(columns={'id': 'object_id'}, inplace=True)
agg_df_ts_flux_by_flux_ratio_sq = agg_df_ts_flux_by_flux_ratio_sq.reset_index(
)
agg_df_ts_flux_by_flux_ratio_sq.rename(columns={'id': 'object_id'},
inplace=True)
agg_df_mjd = agg_df_mjd.reset_index()
agg_df_mjd.rename(columns={'id': 'object_id'}, inplace=True)
agg_df_ts = pd.concat([
agg_df,
agg_df_ts_flux_passband.drop(labels=['object_id'], axis=1),
agg_df_ts_flux.drop(labels=['object_id'], axis=1),
agg_df_ts_flux_by_flux_ratio_sq.drop(labels=['object_id'], axis=1),
agg_df_mjd.drop(labels=['object_id'], axis=1)
],
axis=1).reset_index()
if 'index' in agg_df_ts:
del agg_df_ts['index']
result = agg_df_ts.merge(right=meta, how='outer', on=['object_id'])
return result
print('Extracting PCA data from index files')
PCA_data, lons, lats = get_PCA_data(var_name, rcp, years, data_dir)
num_lons = lons.shape[0]
num_lats = lats.shape[0]
print('DATA MATRIX ' + str(PCA_data.shape))
# Scale PCA data
# Gives zero/NaN/infinity error
#PCA_data = scale_linear_bycolumn(PCA_data, high=1.0, low=0.0)
print('Computing component matrix')
num_comps = 6
comp_indices = []
pca = PCA(n_components=num_comps)
X_pca = pca.fit_transform(PCA_data) #(5490, 3)
# Projection of original data onto component space
corr_array = pca.inverse_transform(X_pca) #(5490, 103968)
components = pca.components_.transpose() # (103968, 3)
print('VARIANCE EXPLAINED: ')
print (pca.explained_variance_ratio_)
rotated_components = varimax(components).transpose() #(3, 103968)
dates_dt = []
dates_ts = []
for year in years:
dates_dt.append(dt.datetime(year,12,1))
dates_ts.append(datetime_to_date(dates_dt[-1], '-'))
for doy_idx in range(1,90):
dates_dt.append(advance_date(dates_dt[-1],1, 'forward'))
dates_ts.append(datetime_to_date(dates_dt[-1], '-'))
def perform_PCA(data, number_of_components):
#print ("Data",data.shape)
pca = PCA(n_components=number_of_components)
#print (pca.fit_transform(data).shape)
return (pca.fit_transform(data).transpose())
def perform_PCA(data, number_of_components):
pca = PCA(n_components=number_of_components)
return (pca.fit_transform(data))
print('PROCESSSING VARIABLE ' + var_name)
print('Extracting PCA data from index files')
LIVNEH_PCA_data, LIVNEH_lons, LIVNEH_lats = get_LIVNEH_PCA_data(var_name, livneh_years, livneh_data_dir)
LOCA_PCA_data, LOCA_lons, LOCA_lats = get_LOCA_PCA_data(rcp, var_name, loca_years, loca_data_dir)
num_lons = LIVNEH_lons.shape[0]
num_lats = LIVNEH_lats.shape[0]
print('LIVNEH DATA MATRIX ' + str(LIVNEH_PCA_data.shape))
print('LOCA DATA MATRIX ' + str(LOCA_PCA_data.shape))
#print('Minutes elapsed: ' + str((dt.datetime.now() - start_time).total_seconds() / 60.0))
print('Computing component matrix')
num_comps = 6
comp_indices = []
pca = PCA(n_components=num_comps)
X_pca = pca.fit_transform(LIVNEH_PCA_data) #(5490, 3)
# Projection of original data onto component space
corr_array = pca.inverse_transform(X_pca) #(5490, 103968)
components = pca.components_.transpose() # (103968, 3)
print('VARIANCE EXPLAINED: ')
print (pca.explained_variance_ratio_)
rotated_components = varimax(components).transpose() #(3, 103968)
dates_dt = []
dates_ts = []
for year in loca_years:
dates_dt.append(dt.datetime(year,12,1))
dates_ts.append(datetime_to_date(dates_dt[-1], '-'))
for doy_idx in range(1,90):
dates_dt.append(advance_date(dates_dt[-1],1, 'forward'))
dates_ts.append(datetime_to_date(dates_dt[-1], '-'))
# if np.isnan(0) == False: # print(100) print(ddd.shape) ddd_scaled = scale(ddd) print(ddd_scaled.shape) # a= np.array(data[]) pca = PCA(n_components=300) results = pca.fit(ddd_scaled) print(pca.explained_variance_ratio_) a = pca.fit_transform(ddd_scaled) aa = pd.Series(a[:, 0].tolist()) data["PCA1"] = aa.values aa = pd.Series(a[:, 1].tolist()) data["PCA2"] = aa.values aa = pd.Series(a[:, 2].tolist()) data["PCA3"] = aa.values aa = pd.Series(a[:, 3].tolist()) data["PCA4"] = aa.values aa = pd.Series(a[:, 4].tolist()) data["PCA5"] = aa.values aa = pd.Series(a[:, 5].tolist()) data["PCA6"] = aa.values aa = pd.Series(a[:, 6].tolist()) data["PCA7"] = aa.values
PCA_data, lons, lats = get_PCA_data(var_name, years, data_dir)
num_lons = lons.shape[0]
num_lats = lats.shape[0]
print('DATA MATRIX ' + str(PCA_data.shape))
# Scale PCA data
# Gives zero/NaN/infinity error
#PCA_data = scale_linear_bycolumn(PCA_data, high=1.0, low=0.0)
#print('Minutes elapsed: ' + str((dt.datetime.now() - start_time).total_seconds() / 60.0))
print('Computing component matrix')
num_comps = 6
comp_indices = []
pca = PCA(n_components=num_comps)
X_pca = pca.fit_transform(PCA_data) #(5490, 3)
# Projection of original data onto component space
corr_array = pca.inverse_transform(X_pca) #(5490, 103968)
components = pca.components_.transpose() # (103968, 3)
print('VARIANCE EXPLAINED: ')
print (pca.explained_variance_ratio_)
rotated_components = varimax(components).transpose() #(3, 103968)
dates_dt = []
dates_ts = []
for year in years:
dates_dt.append(dt.datetime(year,12,1))
dates_ts.append(datetime_to_date(dates_dt[-1], '-'))
for doy_idx in range(1,90):
dates_dt.append(advance_date(dates_dt[-1],1, 'forward'))
dates_ts.append(datetime_to_date(dates_dt[-1], '-'))