--- title: utils.dedimensions keywords: fastai sidebar: home_sidebar nb_path: "00_utils_rd.ipynb" ---
{% raw %}
{% endraw %} {% raw %}
{% endraw %} {% raw %}

reduce_dimensional[source]

reduce_dimensional(df, method=PCA, n_components=2)

{% endraw %} {% raw %}
{% endraw %} {% raw %}
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import datasets

iris = datasets.load_iris()

X = iris.data
y = iris.target

def scatter_plots_for_reduce_dimensional(df,x,y,output=None,hue=None,size=None,style=None,xlabel=None,ylabel=None,title=None):
    fig, ax = plt.subplots()
    ax = sns.scatterplot(x=x, y=y, data=df,hue=hue,size=size,style=style)
    if xlabel is not None:
        ax.set_xlabel(xlabel)
    if ylabel is not None:
        ax.set_ylabel(ylabel)
    if title is not None:
        ax.set_title(title)
    if output is not None:

        fig.savefig(output,dpi=300)


for i in [PCA,MDS,TSNE,UMAP,PHATE]:
    rd_data,reducer = reduce_dimensional(X,i,3)
    rd_data['target'] = y
    scatter_plots_for_reduce_dimensional(rd_data,getattr(i,'__name__')+'1',
                                     getattr(i,'__name__')+'2',hue='target')
Calculating PHATE...
  Running PHATE on 150 observations and 4 variables.
  Calculating graph and diffusion operator...
    Calculating KNN search...
    Calculated KNN search in 0.01 seconds.
    Calculating affinities...
  Calculated graph and diffusion operator in 0.02 seconds.
  Calculating optimal t...
    Automatically selected t = 20
  Calculated optimal t in 0.01 seconds.
  Calculating diffusion potential...
  Calculating metric MDS...
/Users/logan/opt/anaconda3/envs/dev/lib/python3.7/site-packages/graphtools/graphs.py:284: RuntimeWarning: Detected zero distance between samples 101 and 142. Consider removing duplicates to avoid errors in downstream processing.
  RuntimeWarning,
/Users/logan/opt/anaconda3/envs/dev/lib/python3.7/site-packages/sklearn/utils/validation.py:71: FutureWarning: Pass n_neighbors=150 as keyword args. From version 0.25 passing these as positional arguments will result in an error
  FutureWarning)
  Calculated metric MDS in 0.19 seconds.
Calculated PHATE in 0.24 seconds.
{% endraw %} {% raw %}
pd.DataFrame(X,columns=['f1','f2','f3','f4']).to_csv('tests/test.csv')
{% endraw %} {% raw %}
pd.DataFrame(y,columns=['targets']).to_csv('tests/test_anno.csv')
{% endraw %} {% raw %}
!bash build.bash
Converted 00_utils_rd.ipynb.
Converted 01_pipelines.ipynb.
Converted 02_mains.ipynb.
Converted 03_utils_plots.ipynb.
Converted 04_utils_clusters.ipynb.
Converted 05_default.ipynb.
Converted 06_preprocess.ipynb.
Converted 07_merge_degfc.ipynb.
Converted 08_polya_sites.ipynb.
Converted 09_utils_multirun.ipynb.
Converted 10_utils_bamfilter.ipynb.
Converted 11_plots.ipynb.
Converted index.ipynb.
converting: /home/logan/Codes/bitk/05_default.ipynb
converting: /home/logan/Codes/bitk/index.ipynb
converting: /home/logan/Codes/bitk/03_utils_plots.ipynb
converting: /home/logan/Codes/bitk/04_utils_clusters.ipynb
converting: /home/logan/Codes/bitk/02_mains.ipynb
converting: /home/logan/Codes/bitk/09_utils_multirun.ipynb
converting: /home/logan/Codes/bitk/07_merge_degfc.ipynb
converting: /home/logan/Codes/bitk/06_preprocess.ipynb
converting: /home/logan/Codes/bitk/01_pipelines.ipynb
converting: /home/logan/Codes/bitk/08_polya_sites.ipynb
converting: /home/logan/Codes/bitk/11_plots.ipynb
converting: /home/logan/Codes/bitk/10_utils_bamfilter.ipynb
converting: /home/logan/Codes/bitk/00_utils_rd.ipynb
converting /home/logan/Codes/bitk/index.ipynb to README.md
{% endraw %}