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How to find data orientation atfer an ICA

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1

1

I'm trying to cluster tractor sensor data by row in a python script. For the moment I'm trying to find data orientation after a FastICA (sklearn), vertical or horizontal. Because for same data, FastICA can orientate results differently.

Until now, I was searching for an empirical rule with the 2 matrix FastICA().components_ and FastICA().mixing_. But I found nothing that works.

Is somebody have an better idea?

Here is data and my code below:

import geopandas as gpd

import numpy as np



shp_name = 'rawdata379'



shp_source = ('/home/jovyan/scripts/physiocap/Test_Data/{}.shp'.format(shp_name))

source = gpd.read_file(shp_source)

source = source.to_crs({'init' :'epsg:3857'})



src_coord = source[['LONGITUDE', 'LATITUDE']]



from sklearn.decomposition import FastICA

import pandas as pd

from shapely.geometry import Point



rng = np.random.RandomState(42)



pca = FastICA(n_components=2, algorithm='parallel', whiten=True, max_iter=100)

src_coord_pca = pca.fit_transform(src_coord)



src_coord_pca_df = pd.DataFrame(src_coord_pca).rename(columns={0:'X', 1:'Y'})

src_coord_pca_df['geometry'] = list(zip(src_coord_pca_df['X'], src_coord_pca_df['Y']))

src_coord_pca_df['geometry'] = src_coord_pca_df['geometry'].apply(Point)

src_coord_pca_gdf = gpd.GeoDataFrame(src_coord_pca_df, geometry='geometry')

src_coord_pca_gdf.plot(figsize=(9, 9))



compo, feat = pca.components_

print('compo :', compo)

print('feat :', feat)



feat, compo = pca.mixing_

print('compo :', compo)

print('feat :', feat)





#trial of empirical rule

if (pca.components_[0][0] > 0 != pca.components_[0][1] > 0):

    print('vertical')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[0]



else:

    print('horizontal')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[1]

python scikit-learn pca

share|improve this question

asked 4 hours ago

Tim C.Tim C.

463417

add a comment | 

1

1

I'm trying to cluster tractor sensor data by row in a python script. For the moment I'm trying to find data orientation after a FastICA (sklearn), vertical or horizontal. Because for same data, FastICA can orientate results differently.

Until now, I was searching for an empirical rule with the 2 matrix FastICA().components_ and FastICA().mixing_. But I found nothing that works.

Is somebody have an better idea?

Here is data and my code below:

import geopandas as gpd

import numpy as np



shp_name = 'rawdata379'



shp_source = ('/home/jovyan/scripts/physiocap/Test_Data/{}.shp'.format(shp_name))

source = gpd.read_file(shp_source)

source = source.to_crs({'init' :'epsg:3857'})



src_coord = source[['LONGITUDE', 'LATITUDE']]



from sklearn.decomposition import FastICA

import pandas as pd

from shapely.geometry import Point



rng = np.random.RandomState(42)



pca = FastICA(n_components=2, algorithm='parallel', whiten=True, max_iter=100)

src_coord_pca = pca.fit_transform(src_coord)



src_coord_pca_df = pd.DataFrame(src_coord_pca).rename(columns={0:'X', 1:'Y'})

src_coord_pca_df['geometry'] = list(zip(src_coord_pca_df['X'], src_coord_pca_df['Y']))

src_coord_pca_df['geometry'] = src_coord_pca_df['geometry'].apply(Point)

src_coord_pca_gdf = gpd.GeoDataFrame(src_coord_pca_df, geometry='geometry')

src_coord_pca_gdf.plot(figsize=(9, 9))



compo, feat = pca.components_

print('compo :', compo)

print('feat :', feat)



feat, compo = pca.mixing_

print('compo :', compo)

print('feat :', feat)





#trial of empirical rule

if (pca.components_[0][0] > 0 != pca.components_[0][1] > 0):

    print('vertical')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[0]



else:

    print('horizontal')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[1]

python scikit-learn pca

share|improve this question

asked 4 hours ago

Tim C.Tim C.

463417

add a comment | 

I'm trying to cluster tractor sensor data by row in a python script. For the moment I'm trying to find data orientation after a FastICA (sklearn), vertical or horizontal. Because for same data, FastICA can orientate results differently.

Until now, I was searching for an empirical rule with the 2 matrix FastICA().components_ and FastICA().mixing_. But I found nothing that works.

Is somebody have an better idea?

Here is data and my code below:

import geopandas as gpd

import numpy as np



shp_name = 'rawdata379'



shp_source = ('/home/jovyan/scripts/physiocap/Test_Data/{}.shp'.format(shp_name))

source = gpd.read_file(shp_source)

source = source.to_crs({'init' :'epsg:3857'})



src_coord = source[['LONGITUDE', 'LATITUDE']]



from sklearn.decomposition import FastICA

import pandas as pd

from shapely.geometry import Point



rng = np.random.RandomState(42)



pca = FastICA(n_components=2, algorithm='parallel', whiten=True, max_iter=100)

src_coord_pca = pca.fit_transform(src_coord)



src_coord_pca_df = pd.DataFrame(src_coord_pca).rename(columns={0:'X', 1:'Y'})

src_coord_pca_df['geometry'] = list(zip(src_coord_pca_df['X'], src_coord_pca_df['Y']))

src_coord_pca_df['geometry'] = src_coord_pca_df['geometry'].apply(Point)

src_coord_pca_gdf = gpd.GeoDataFrame(src_coord_pca_df, geometry='geometry')

src_coord_pca_gdf.plot(figsize=(9, 9))



compo, feat = pca.components_

print('compo :', compo)

print('feat :', feat)



feat, compo = pca.mixing_

print('compo :', compo)

print('feat :', feat)





#trial of empirical rule

if (pca.components_[0][0] > 0 != pca.components_[0][1] > 0):

    print('vertical')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[0]



else:

    print('horizontal')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[1]

python scikit-learn pca

share|improve this question

asked 4 hours ago

Tim C.Tim C.

463417

import geopandas as gpd

import numpy as np



shp_name = 'rawdata379'



shp_source = ('/home/jovyan/scripts/physiocap/Test_Data/{}.shp'.format(shp_name))

source = gpd.read_file(shp_source)

source = source.to_crs({'init' :'epsg:3857'})



src_coord = source[['LONGITUDE', 'LATITUDE']]



from sklearn.decomposition import FastICA

import pandas as pd

from shapely.geometry import Point



rng = np.random.RandomState(42)



pca = FastICA(n_components=2, algorithm='parallel', whiten=True, max_iter=100)

src_coord_pca = pca.fit_transform(src_coord)



src_coord_pca_df = pd.DataFrame(src_coord_pca).rename(columns={0:'X', 1:'Y'})

src_coord_pca_df['geometry'] = list(zip(src_coord_pca_df['X'], src_coord_pca_df['Y']))

src_coord_pca_df['geometry'] = src_coord_pca_df['geometry'].apply(Point)

src_coord_pca_gdf = gpd.GeoDataFrame(src_coord_pca_df, geometry='geometry')

src_coord_pca_gdf.plot(figsize=(9, 9))



compo, feat = pca.components_

print('compo :', compo)

print('feat :', feat)



feat, compo = pca.mixing_

print('compo :', compo)

print('feat :', feat)





#trial of empirical rule

if (pca.components_[0][0] > 0 != pca.components_[0][1] > 0):

    print('vertical')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[0]



else:

    print('horizontal')

    src_coord_pca_df = pd.DataFrame(src_coord_pca)[1]

share|improve this question

asked 4 hours ago

Tim C.Tim C.

463417

share|improve this question

asked 4 hours ago

Tim C.Tim C.

463417

asked 4 hours ago

Tim C.Tim C.

463417

asked 4 hours ago

Tim C.Tim C.

463417