Revert "Merge branch 'cristian_lenta-ba_code' into 'master'"
This reverts merge request !1
This commit is contained in:
Cristian Lenta
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import os
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from experiment import Experiment
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# noinspection PyUnresolvedReferences
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from soup import Soup
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from argparse import ArgumentParser
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import numpy as np
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import plotly as pl
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import plotly.graph_objs as go
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import colorlover as cl
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import dill
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from sklearn.manifold.t_sne import TSNE, PCA
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def build_args():
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arg_parser = ArgumentParser()
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arg_parser.add_argument('-i', '--in_file', nargs=1, type=str)
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arg_parser.add_argument('-o', '--out_file', nargs='?', default='out', type=str)
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return arg_parser.parse_args()
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def build_from_soup_or_exp(soup):
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particles = soup.historical_particles
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particle_list = []
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for particle in particles.values():
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particle_dict = dict(
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trajectory=[event['weights'] for event in particle],
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time=[event['time'] for event in particle],
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action=[event.get('action', None) for event in particle],
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counterpart=[event.get('counterpart', None) for event in particle]
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)
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if any([x is not None for x in particle_dict['counterpart']]):
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print('counterpart')
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particle_list.append(particle_dict)
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return particle_list
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def plot_latent_trajectories(soup_or_experiment, filename='latent_trajectory_plot'):
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assert isinstance(soup_or_experiment, (Experiment, Soup))
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bupu = cl.scales['11']['div']['RdYlGn']
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data_dict = build_from_soup_or_exp(soup_or_experiment)
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scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins
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# Fit the mebedding space
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transformer = TSNE()
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for particle_dict in data_dict:
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array = np.asarray([np.hstack([x.flatten() for x in timestamp]).flatten()
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for timestamp in particle_dict['trajectory']])
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particle_dict['trajectory'] = array
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transformer.fit(array)
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# Transform data accordingly and plot it
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data = []
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for p_id, particle_dict in enumerate(data_dict):
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transformed = transformer._fit(np.asarray(particle_dict['trajectory']))
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line_trace = go.Scatter(
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x=transformed[:, 0],
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y=transformed[:, 1],
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text='Hovertext goes here'.format(),
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line=dict(color=scale[p_id]),
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# legendgroup='Position -{}'.format(pos),
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name='Particle - {}'.format(p_id),
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showlegend=True,
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# hoverinfo='text',
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mode='lines')
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line_start = go.Scatter(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]],
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marker=dict(
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color='rgb(255, 0, 0)',
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size=4
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),
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showlegend=False
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)
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line_end = go.Scatter(mode='markers', x=[transformed[-1, 0]], y=[transformed[-1, 1]],
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marker=dict(
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color='rgb(0, 0, 0)',
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size=4
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),
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showlegend=False
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)
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data.extend([line_trace, line_start, line_end])
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layout = dict(title='{} - Latent Trajectory Movement'.format('Penis'),
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height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
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# import plotly.io as pio
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# pio.write_image(fig, filename)
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
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def norm(val, a=0, b=0.25):
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return (val - a) / (b - a)
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data_list = build_from_soup_or_exp(soup_or_experiment)
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if not data_list:
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return
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base_scale = cl.scales['9']['div']['RdYlGn']
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# base_scale = cl.scales['9']['qual']['Set1']
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scale = cl.interp(base_scale, len(data_list)+1) # Map color scale to N bins
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# Fit the embedding space
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transformer = PCA(n_components=2)
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array = []
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for particle_dict in data_list:
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array.append(particle_dict['trajectory'])
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transformer.fit(np.vstack(array))
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# Transform data accordingly and plot it
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data = []
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for p_id, particle_dict in enumerate(data_list):
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transformed = transformer.transform(particle_dict['trajectory'])
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line_trace = go.Scatter3d(
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x=transformed[:, 0],
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y=transformed[:, 1],
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z=np.asarray(particle_dict['time']),
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text='Particle: {}<br> It had {} lifes.'.format(p_id, len(particle_dict['trajectory'])),
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line=dict(
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color=scale[p_id],
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width=4
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),
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# legendgroup='Particle - {}'.format(p_id),
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name='Particle -{}'.format(p_id),
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showlegend=False,
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hoverinfo='text',
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mode='lines')
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line_start = go.Scatter3d(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]],
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z=np.asarray(particle_dict['time'][0]),
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marker=dict(
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color='rgb(255, 0, 0)',
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size=4
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),
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showlegend=False
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)
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line_end = go.Scatter3d(mode='markers', x=[transformed[-1, 0]], y=[transformed[-1, 1]],
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z=np.asarray(particle_dict['time'][-1]),
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marker=dict(
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color='rgb(0, 0, 0)',
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size=4
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),
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showlegend=False
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)
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data.extend([line_trace, line_start, line_end])
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axis_layout = dict(gridcolor='rgb(255, 255, 255)',
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gridwidth=3,
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zerolinecolor='rgb(255, 255, 255)',
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showbackground=True,
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backgroundcolor='rgb(230, 230,230)',
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titlefont=dict(
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color='black',
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size=30
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)
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)
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layout = go.Layout(scene=dict(
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# aspectratio=dict(x=2, y=2, z=2),
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xaxis=dict(title='Transformed X', **axis_layout),
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yaxis=dict(title='Transformed Y', **axis_layout),
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zaxis=dict(title='Epoch', **axis_layout)),
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# title='{} - Latent Trajectory Movement'.format('Soup'),
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width=1024, height=1024,
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margin=dict(l=0, r=0, b=0, t=0)
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)
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename, validate=True)
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pass
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def plot_histogram(bars_dict_list, filename='histogram_plot'):
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# catagorical
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ryb = cl.scales['10']['div']['RdYlBu']
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data = []
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for bar_id, bars_dict in bars_dict_list:
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hist = go.Histogram(
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histfunc="count",
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y=bars_dict.get('value', 14),
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x=bars_dict.get('name', 'gimme a name'),
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showlegend=False,
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marker=dict(
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color=ryb[bar_id]
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),
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)
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data.append(hist)
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layout=dict(title='{} Histogram Plot'.format('Experiment Name Penis'),
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height=400, width=400, margin=dict(l=0, r=0, t=0, b=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def line_plot(line_dict_list, filename='lineplot'):
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# lines with standard deviation
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# Transform data accordingly and plot it
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data = []
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rdylgn = cl.scales['10']['div']['RdYlGn']
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rdylgn_background = [scale + (0.4,) for scale in cl.to_numeric(rdylgn)]
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for line_id, line_dict in enumerate(line_dict_list):
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name = line_dict.get('name', 'gimme a name')
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upper_bound = go.Scatter(
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name='Upper Bound',
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x=line_dict['x'],
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y=line_dict['upper_y'],
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mode='lines',
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marker=dict(color="#444"),
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line=dict(width=0),
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fillcolor=rdylgn_background[line_id],
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)
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trace = go.Scatter(
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x=line_dict['x'],
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y=line_dict['main_y'],
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mode='lines',
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name=name,
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line=dict(color=line_id),
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fillcolor=rdylgn_background[line_id],
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fill='tonexty')
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lower_bound = go.Scatter(
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name='Lower Bound',
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x=line_dict['x'],
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y=line_dict['lower_y'],
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marker=dict(color="#444"),
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line=dict(width=0),
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mode='lines')
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data.extend([upper_bound, trace, lower_bound])
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layout=dict(title='{} Line Plot'.format('Experiment Name Penis'),
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height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
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fig = go.Figure(data=data, layout=layout)
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pl.offline.plot(fig, auto_open=True, filename=filename)
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pass
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def search_and_apply(absolut_file_or_folder, plotting_function, files_to_look_for=[]):
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if os.path.isdir(absolut_file_or_folder):
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for sub_file_or_folder in os.scandir(absolut_file_or_folder):
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search_and_apply(sub_file_or_folder.path, plotting_function, files_to_look_for=files_to_look_for)
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elif absolut_file_or_folder.endswith('.dill'):
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file_or_folder = os.path.split(absolut_file_or_folder)[-1]
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if file_or_folder in files_to_look_for and not os.path.exists('{}.html'.format(absolut_file_or_folder[:-5])):
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print('Apply Plotting function "{func}" on file "{file}"'.format(func=plotting_function.__name__,
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file=absolut_file_or_folder)
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)
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with open(absolut_file_or_folder, 'rb') as in_f:
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exp = dill.load(in_f)
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try:
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plotting_function(exp, filename='{}.html'.format(absolut_file_or_folder[:-5]))
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except ValueError:
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pass
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except AttributeError:
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pass
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else:
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# This was either another FilyType or Plot.html alerady exists.
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pass
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if __name__ == '__main__':
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args = build_args()
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in_file = args.in_file[0]
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out_file = args.out_file
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search_and_apply(in_file, plot_latent_trajectories_3D, ["trajectorys.dill", "soup.dill"])
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