separado en dos local_exec docker y local_build_docker. Eliminado xai.py, que no es necesario aqui y da problemas

dvats/xai.py

@@ -1,964 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: ../nbs/xai.ipynb.
-
-# %% auto 0
-__all__ = ['get_embeddings', 'get_dataset', 'umap_parameters', 'get_prjs', 'plot_projections', 'plot_projections_clusters',
-           'calculate_cluster_stats', 'anomaly_score', 'detector', 'plot_anomaly_scores_distribution',
-           'plot_clusters_with_anomalies', 'update_plot', 'plot_clusters_with_anomalies_interactive_plot',
-           'get_df_selected', 'shift_datetime', 'get_dateformat', 'get_anomalies', 'get_anomaly_styles',
-           'InteractiveAnomalyPlot', 'plot_save', 'plot_initial_config', 'merge_overlapping_windows',
-           'InteractiveTSPlot', 'add_selected_features', 'add_windows', 'setup_style', 'toggle_trace',
-           'set_features_buttons', 'move_left', 'move_right', 'move_down', 'move_up', 'delta_x_bigger',
-           'delta_y_bigger', 'delta_x_lower', 'delta_y_lower', 'add_movement_buttons', 'setup_boxes', 'initial_plot',
-           'show']
-
-# %% ../nbs/xai.ipynb 1
-#Weight & Biases
-import wandb
-
-#Yaml
-from yaml import load, FullLoader
-
-#Embeddings
-from .all import *
-from tsai.data.preparation import prepare_forecasting_data
-from tsai.data.validation import get_forecasting_splits
-from fastcore.all import *
-
-#Dimensionality reduction
-from tsai.imports import *
-
-#Clustering
-import hdbscan
-import time
-from .dr import get_PCA_prjs, get_UMAP_prjs, get_TSNE_prjs
-
-import seaborn as sns
-import matplotlib.pyplot as plt
-import pandas as pd
-import ipywidgets as widgets
-from IPython.display import display
-from functools import partial
-
-from IPython.display import display, clear_output, HTML as IPHTML
-from ipywidgets import Button, Output, VBox, HBox, HTML, Layout, FloatSlider
-
-import plotly.graph_objs as go
-import plotly.offline as py
-import plotly.io as pio
-#! pip install kaleido
-import kaleido
-
-
-# %% ../nbs/xai.ipynb 4
-def get_embeddings(config_lrp, run_lrp, api, print_flag = False):
-    artifacts_gettr = run_lrp.use_artifact if config_lrp.use_wandb else api.artifact
-    emb_artifact = artifacts_gettr(config_lrp.emb_artifact, type='embeddings')
-    if print_flag: print(emb_artifact.name)
-    emb_config = emb_artifact.logged_by().config
-    return emb_artifact.to_obj(), emb_artifact, emb_config
-
-# %% ../nbs/xai.ipynb 5
-def get_dataset(
-    config_lrp, 
-    config_emb,
-    config_dr,
-    run_lrp,
-    api, 
-    print_flag = False
-):
-    # Botch to use artifacts offline
-    artifacts_gettr = run_lrp.use_artifact if config_lrp.use_wandb else api.artifact
-    enc_artifact = artifacts_gettr(config_emb['enc_artifact'], type='learner')
-    if print_flag: print (enc_artifact.name)
-    ## TODO: This only works when you run it two timeS! WTF?
-    try:
-        enc_learner = enc_artifact.to_obj()
-    except:
-        enc_learner = enc_artifact.to_obj()
-    
-    ## Restore artifact
-    enc_logger = enc_artifact.logged_by()
-    enc_artifact_train = artifacts_gettr(enc_logger.config['train_artifact'], type='dataset')
-    #cfg_.show_attrdict(enc_logger.config)
-    if enc_logger.config['valid_artifact'] is not None:
-        enc_artifact_valid = artifacts_gettr(enc_logger.config['valid_artifact'], type='dataset')
-        if print_flag: print("enc_artifact_valid:", enc_artifact_valid.name)
-    if print_flag: print("enc_artifact_train: ", enc_artifact_train.name)
-    
-    if config_dr['dr_artifact'] is not None:
-        print("Is not none")
-        dr_artifact = artifacts_gettr(config_dr['enc_artifact'])
-    else:
-        dr_artifact = enc_artifact_train
-    if print_flag: print("DR artifact train: ", dr_artifact.name)
-    if print_flag: print("--> DR artifact name", dr_artifact.name)
-    dr_artifact
-    df = dr_artifact.to_df()
-    if print_flag: print("--> DR After to df", df.shape)
-    if print_flag: display(df.head())
-    return df, dr_artifact, enc_artifact, enc_learner
-
-# %% ../nbs/xai.ipynb 6
-def umap_parameters(config_dr, config):
-    umap_params_cpu = {
-        'n_neighbors' : config_dr.n_neighbors,
-        'min_dist' : config_dr.min_dist,
-        'random_state': np.uint64(822569775), 
-        'metric': config_dr.metric,
-        #'a': 1.5769434601962196,
-        #'b': 0.8950608779914887,
-        #'metric_kwds': {'p': 2}, #No debería ser necesario, just in case
-        #'output_metric': 'euclidean',
-        'verbose': 4,
-        #'n_epochs': 200
-    }
-    umap_params_gpu = {
-        'n_neighbors' : config_dr.n_neighbors,
-        'min_dist' : config_dr.min_dist,
-        'random_state': np.uint64(1234), 
-        'metric': config_dr.metric,
-        'a': 1.5769434601962196,
-        'b': 0.8950608779914887,
-        'target_metric': 'euclidean',
-        'target_n_neighbors': config_dr.n_neighbors,
-        'verbose': 4, #6, #CUML_LEVEL_TRACE
-        'n_epochs': 200*3*2,
-        'init': 'random',
-        'hash_input': True
-    }
-    if config_dr.cpu_flag: 
-        umap_params = umap_params_cpu
-    else:
-        umap_params = umap_params_gpu
-    return umap_params
-
-# %% ../nbs/xai.ipynb 7
-def get_prjs(embs_no_nan, config_dr, config, print_flag = False):
-    umap_params = umap_parameters(config_dr, config)
-    prjs_pca = get_PCA_prjs(
-        X    = embs_no_nan, 
-        cpu  = False,  
-        print_flag = print_flag, 
-        **umap_params
-    )
-    if print_flag: 
-        print(prjs_pca.shape)
-    prjs_umap = get_UMAP_prjs(
-        input_data = prjs_pca, 
-        cpu =  config_dr.cpu_flag, #config_dr.cpu, 
-        print_flag = print_flag,         
-        **umap_params
-    )
-    if print_flag: prjs_umap.shape
-    return prjs_umap
-
-# %% ../nbs/xai.ipynb 9
-def plot_projections(prjs, umap_params, fig_size = (25,25)):
-    "Plot 2D projections thorugh a connected scatter plot"
-    df_prjs = pd.DataFrame(prjs, columns = ['x1', 'x2'])
-    fig = plt.figure(figsize=(fig_size[0],fig_size[1]))
-    ax = fig.add_subplot(111)
-    ax.scatter(df_prjs['x1'], df_prjs['x2'], marker='o', facecolors='none', edgecolors='b', alpha=0.1)
-    ax.plot(df_prjs['x1'], df_prjs['x2'], alpha=0.5, picker=1)
-    plt.title('DR params -  n_neighbors:{:d} min_dist:{:f}'.format(
-        umap_params['n_neighbors'],umap_params['min_dist']))
-    return ax
-
-# %% ../nbs/xai.ipynb 10
-def plot_projections_clusters(prjs, clusters_labels, umap_params, fig_size = (25,25)):
-    "Plot 2D projections thorugh a connected scatter plot"
-    df_prjs = pd.DataFrame(prjs, columns = ['x1', 'x2'])
-    df_prjs['cluster'] = clusters_labels
-    
-    fig = plt.figure(figsize=(fig_size[0],fig_size[1]))
-    ax = fig.add_subplot(111)
-    
-    # Create a scatter plot for each cluster with different colors
-    unique_labels = df_prjs['cluster'].unique()
-    print(unique_labels)
-    for label in unique_labels:
-        cluster_data = df_prjs[df_prjs['cluster'] == label]
-        ax.scatter(cluster_data['x1'], cluster_data['x2'], label=f'Cluster {label}')
-        #ax.scatter(df_prjs['x1'], df_prjs['x2'], marker='o', facecolors='none', edgecolors='b', alpha=0.1)
-    
-    #ax.plot(df_prjs['x1'], df_prjs['x2'], alpha=0.5, picker=1)
-    plt.title('DR params -  n_neighbors:{:d} min_dist:{:f}'.format(
-        umap_params['n_neighbors'],umap_params['min_dist']))
-    return ax
-
-# %% ../nbs/xai.ipynb 11
-def calculate_cluster_stats(data, labels):
-    """Computes the media and the standard deviation for every cluster."""
-    cluster_stats = {}
-    for label in np.unique(labels):
-        #members = data[labels == label]
-        members = data
-        mean = np.mean(members, axis = 0)
-        std = np.std(members, axis = 0)
-        cluster_stats[label] = (mean, std)
-    return cluster_stats
-
-# %% ../nbs/xai.ipynb 12
-def anomaly_score(point, cluster_stats, label):
-    """Computes an anomaly score for each point."""
-    mean, std = cluster_stats[label]
-    return np.linalg.norm((point - mean) / std)
-
-# %% ../nbs/xai.ipynb 13
-def detector(data, labels):
-    """Anomaly detection function."""
-    cluster_stats = calculate_cluster_stats(data, labels)
-    scores = []
-    for point, label in zip(data, labels):
-        score = anomaly_score(point, cluster_stats, label)
-        scores.append(score)
-    return np.array(scores)
-
-# %% ../nbs/xai.ipynb 15
-def plot_anomaly_scores_distribution(anomaly_scores):
-    "Plot the distribution of anomaly scores to check for normality"
-    plt.figure(figsize=(10, 6))
-    sns.histplot(anomaly_scores, kde=True, bins=30)
-    plt.title("Distribución de Anomaly Scores")
-    plt.xlabel("Anomaly Score")
-    plt.ylabel("Frecuencia")
-    plt.show()
-
-# %% ../nbs/xai.ipynb 16
-def plot_clusters_with_anomalies(prjs, clusters_labels, anomaly_scores, threshold, fig_size=(25, 25)):
-    "Plot 2D projections of clusters and superimpose anomalies"
-    df_prjs = pd.DataFrame(prjs, columns=['x1', 'x2'])
-    df_prjs['cluster'] = clusters_labels
-    df_prjs['anomaly'] = anomaly_scores > threshold
-
-    fig = plt.figure(figsize=(fig_size[0], fig_size[1]))
-    ax = fig.add_subplot(111)
-
-    # Plot each cluster with different colors
-    unique_labels = df_prjs['cluster'].unique()
-    for label in unique_labels:
-        cluster_data = df_prjs[df_prjs['cluster'] == label]
-        ax.scatter(cluster_data['x1'], cluster_data['x2'], label=f'Cluster {label}', alpha=0.7)
-
-    # Superimpose anomalies
-    anomalies = df_prjs[df_prjs['anomaly']]
-    ax.scatter(anomalies['x1'], anomalies['x2'], color='red', label='Anomalies', edgecolor='k', s=50)
-
-    plt.title('Clusters and anomalies')
-    plt.legend()
-    plt.show()
-
-def update_plot(threshold, prjs_umap, clusters_labels, anomaly_scores, fig_size):
-    plot_clusters_with_anomalies(prjs_umap, clusters_labels, anomaly_scores, threshold, fig_size)
-
-def plot_clusters_with_anomalies_interactive_plot(threshold, prjs_umap, clusters_labels, anomaly_scores, fig_size):
-    threshold_slider = widgets.FloatSlider(value=threshold, min=0.001, max=3, step=0.001, description='Threshold')
-    interactive_plot =  widgets.interactive(update_plot, threshold = threshold_slider, 
-                              prjs_umap = widgets.fixed(prjs_umap), 
-                              clusters_labels = widgets.fixed(clusters_labels),
-                              anomaly_scores = widgets.fixed(anomaly_scores),
-                              fig_size = widgets.fixed((25,25)))
-    display(interactive_plot)
-    
-
-# %% ../nbs/xai.ipynb 18
-import plotly.express as px
-from datetime import timedelta
-
-# %% ../nbs/xai.ipynb 19
-def get_df_selected(df, selected_indices, w, stride = 1): #Cuidado con stride
-    '''Links back the selected points to the original dataframe and returns the associated windows indices'''
-    n_windows = len(selected_indices)
-    window_ranges = [(id*stride, (id*stride)+w) for id in selected_indices]    
-    #window_ranges = [(id*w, (id+1)*w+1) for id in selected_indices]    
-    #window_ranges = [(id*stride, (id*stride)+w) for id in selected_indices]
-    #print(window_ranges)
-    valores_tramos = [df.iloc[inicio:fin+1] for inicio, fin in window_ranges]
-    df_selected = pd.concat(valores_tramos, ignore_index=False)
-    return window_ranges, n_windows, df_selected
-
-# %% ../nbs/xai.ipynb 20
-def shift_datetime(dt, seconds, sign, dateformat="%Y-%m-%d %H:%M:%S.%f", print_flag = False):
-    """
-    This function gets a datetime dt, a number of seconds, 
-    a sign and moves the date such number of seconds to the future 
-    if sign is '+' and to the past if sing is '-'.
-    """
-    
-    if print_flag: print(dateformat)
-    dateformat2= "%Y-%m-%d %H:%M:%S.%f"
-    dateformat3 = "%Y-%m-%d"
-    ok = False
-
-    try: 
-        if print_flag: print("dt ", dt, "seconds", seconds, "sign", sign)
-        new_dt = datetime.strptime(dt, dateformat)
-        if print_flag: print("ndt", new_dt)
-        ok = True
-    except ValueError as e:
-        if print_flag: 
-            print("Error: ", e)
-        
-    if (not ok):
-        try:
-            if print_flag: print("Parsing alternative dataformat", dt, "seconds", seconds, "sign", sign, dateformat2)
-            new_dt = datetime.strptime(dt, dateformat3)
-            if print_flag: print("2ndt", new_dt)            
-        except ValueError as e:
-            print("Error: ", e)
-    if print_flag: print(new_dt)
-    try:
-            
-        if new_dt.hour == 0 and new_dt.minute == 0 and new_dt.second == 0:
-            if print_flag: "Aqui"
-            new_dt = new_dt.replace(hour=0, minute=0, second=0, microsecond=0)
-            if print_flag: print(new_dt)
-
-        if print_flag: print("ndt", new_dt)
-                
-        if (sign == '+'):
-            if print_flag: print("Aqui")
-            new_dt = new_dt + timedelta(seconds = seconds)
-            if print_flag: print(new_dt)
-        else: 
-            if print_flag: print(sign, type(dt))
-            new_dt = new_dt - timedelta(seconds = seconds)
-            if print_flag: print(new_dt)            
-        if new_dt.hour == 0 and new_dt.minute == 0 and new_dt.second == 0:
-            if print_flag: print("replacing")
-            new_dt = new_dt.replace(hour=0, minute=0, second=0, microsecond=0)
-        
-        new_dt_str = new_dt.strftime(dateformat2)
-        if print_flag: print("new dt ", new_dt)
-    except ValueError as e:
-        if print_flag: print("Aqui3")
-        shift_datetime(dt, 0, sign, dateformat = "%Y-%m-%d", print_flag = False)
-        return str(e)
-    return new_dt_str
-
-
-
-# %% ../nbs/xai.ipynb 21
-def get_dateformat(text_date):
-    dateformat1 = "%Y-%m-%d %H:%M:%S"
-    dateformat2 = "%Y-%m-%d %H:%M:%S.%f"
-    dateformat3 = "%Y-%m-%d"
-    dateformat = ""
-    parts = text_date.split()
-
-    if len(parts) == 2:
-        time_parts = parts[1].split(':')
-        if len(time_parts) == 3:
-            sec_parts = time_parts[2].split('.')
-            if len(sec_parts) == 2:
-                dateformat = dateformat2
-            else:
-                dateformat = dateformat1
-        else:
-            dateformat = "unknown format 1"
-    elif len(parts) == 1:
-        dateformat = dateformat3
-    else:
-        dateformat = "unknown format 2"
-
-    return dateformat
-
-# %% ../nbs/xai.ipynb 23
-def get_anomalies(df, threshold, flag):
-    df['anomaly'] = [ (score > threshold) and flag for score in df['anomaly_score']]
-    
-def get_anomaly_styles(df, threshold, anomaly_scores, flag = False, print_flag = False):
-        if print_flag: print("Threshold: ", threshold)
-        if print_flag: print("Flag", flag)
-        if print_flag: print("df ~", df.shape)
-        df['anomaly'] = [ (score > threshold) and flag for score in df['anomaly_score'] ]
-        if print_flag: print(df)
-        get_anomalies(df, threshold, flag)
-        anomalies = df[df['anomaly']]
-        if flag:
-            df['anomaly'] = [ 
-                (score > threshold) and flag 
-                for score in anomaly_scores 
-            ]
-            symbols = [
-                'x' if is_anomaly else 'circle' 
-                for is_anomaly in df['anomaly']
-            ]
-            line_colors = [
-                'black'
-                if (is_anomaly and flag) else 'rgba(0,0,0,0)'
-                for is_anomaly in df['anomaly']
-            ]
-        else:
-            symbols = ['circle' for _ in df['x1']]
-            line_colors = ['rgba(0,0,0,0)' for _ in df['x1']]
-        if print_flag: print(anomalies)
-        return symbols, line_colors
-### Example of use
-#prjs_df = pd.DataFrame(prjs_umap, columns = ['x1', 'x2'])
-#prjs_df['anomaly_score'] = anomaly_scores
-#s, l = get_anomaly_styles(prjs_df, 1, True)
-
-# %% ../nbs/xai.ipynb 24
-class InteractiveAnomalyPlot():
-    def __init__(
-        self, selected_indices = [], 
-        threshold = 0.15, 
-        anomaly_flag = False,
-        path = "../imgs", w = 0
-    ):
-        self.selected_indices = selected_indices
-        self.selected_indices_tmp = selected_indices
-        self.threshold = threshold
-        self.threshold_ = threshold
-        self.anomaly_flag = anomaly_flag
-        self.w = w
-        self.name = f"w={self.w}"
-        self.path = f"{path}{self.name}.png"
-        self.interaction_enabled = True
-    
-
-    def plot_projections_clusters_interactive(
-        self, prjs, cluster_labels, umap_params, anomaly_scores=[], fig_size=(7,7), print_flag = False
-    ):
-        self.selected_indices_tmp = self.selected_indices
-        py.init_notebook_mode()
-
-        prjs_df, cluster_colors = plot_initial_config(prjs, cluster_labels, anomaly_scores)
-        legend_items = [widgets.HTML(f'<b>Cluster {cluster}:</b> <span style="color:{color};">■</span>')
-                        for cluster, color in cluster_colors.items()]
-        legend = widgets.VBox(legend_items)
-
-        marker_colors = prjs_df['cluster'].map(cluster_colors)
-    
-        symbols, line_colors = get_anomaly_styles(prjs_df, self.threshold_, anomaly_scores, self.anomaly_flag, print_flag)
-    
-        fig = go.FigureWidget(
-            [
-                go.Scatter(
-                    x=prjs_df['x1'], y=prjs_df['x2'], 
-                    mode="markers", 
-                    marker= {
-                        'color': marker_colors,
-                        'line': { 'color': line_colors, 'width': 1 },
-                        'symbol': symbols
-                    },
-                    text = prjs_df.index
-                )
-            ]
-        )
-
-        line_trace = go.Scatter(
-            x=prjs_df['x1'], 
-            y=prjs_df['x2'], 
-            mode="lines",  
-            line=dict(color='rgba(128, 128, 128, 0.5)', width=1)#,
-            #showlegend=False  # Puedes configurar si deseas mostrar esta línea en la leyenda
-        )
-    
-        fig.add_trace(line_trace)
-
-        sca = fig.data[0]
-        
-        fig.update_layout(
-            dragmode='lasso',
-            width=700, 
-            height=500,
-            title={
-                'text': '<span style="font-weight:bold">DR params - n_neighbors:{:d} min_dist:{:f}</span>'.format(
-                         umap_params['n_neighbors'], umap_params['min_dist']),
-                'y':0.98,
-                'x':0.5,
-                'xanchor': 'center',
-                'yanchor': 'top'
-            },
-            plot_bgcolor='white',
-            paper_bgcolor='#f0f0f0',
-            xaxis=dict(gridcolor='lightgray', zerolinecolor='black', title = 'x'), 
-            yaxis=dict(gridcolor='lightgray', zerolinecolor='black', title = 'y'),
-            margin=dict(l=10, r=20, t=30, b=10)
-        
-        
-        )
-    
-        output_tmp = Output()
-        output_button = Output()
-        output_anomaly = Output()
-        output_threshold = Output()
-        output_width = Output()
-    
-        def select_action(trace, points, selector):
-            self.selected_indices_tmp = points.point_inds
-            with output_tmp:
-                output_tmp.clear_output(wait=True)
-                if print_flag: print("Selected indices tmp:", self.selected_indices_tmp)
-        
-        def button_action(b):
-            self.selected_indices = self.selected_indices_tmp 
-            with output_button: 
-                output_button.clear_output(wait = True)
-                if print_flag: print("Selected indices:", self.selected_indices)
-
-    
-        def update_anomalies():           
-            if print_flag: print("About to update anomalies")
-                
-            symbols, line_colors = get_anomaly_styles(prjs_df, self.threshold_, anomaly_scores, self.anomaly_flag, print_flag)
-
-            if print_flag: print("Anomaly styles got")
-
-            with fig.batch_update():
-                fig.data[0].marker.symbol = symbols
-                fig.data[0].marker.line.color = line_colors
-            if print_flag: print("Anomalies updated")
-            if print_flag: print("Threshold: ", self.threshold_)
-            if print_flag: print("Scores: ", anomaly_scores)
-        
-              
-        def anomaly_action(b):
-            with output_anomaly:  # Cambia output_flag a output_anomaly
-                output_anomaly.clear_output(wait=True)
-                if print_fllag: print("Negate anomaly flag")
-                self.anomaly_flag = not self.anomaly_flag
-                if print_flag: print("Show anomalies:", self.anomaly_flag)
-                update_anomalies()
-                              
-        sca.on_selection(select_action)
-        layout = widgets.Layout(width='auto', height='40px')
-        button = Button(
-            description="Update selected_indices",
-            style = {'button_color': 'lightblue'},
-            display = 'flex',
-            flex_row = 'column',
-            align_items = 'stretch',
-            layout = layout
-        )
-        anomaly_button = Button(
-            description = "Show anomalies",
-            style = {'button_color': 'lightgray'},
-            display = 'flex',
-            flex_row = 'column',
-            align_items = 'stretch',
-            layout = layout
-        )
-        
-        button.on_click(button_action)
-        anomaly_button.on_click(anomaly_action)
-    
-        ##### Reactivity buttons
-        pause_button = Button(
-            description = "Pause interactiveness",
-            style = {'button_color': 'pink'},
-            display = 'flex',
-            flex_row = 'column',
-            align_items = 'stretch',
-            layout = layout
-        )
-        resume_button = Button(
-            description = "Resume interactiveness",
-            style = {'button_color': 'lightgreen'},
-            display = 'flex',
-            flex_row = 'column',
-            align_items = 'stretch',
-            layout = layout
-        )
-
-    
-        threshold_slider = FloatSlider(
-            value=self.threshold_,
-            min=0.0,
-            max=float(np.ceil(self.threshold+5)),
-            step=0.0001,
-            description='Anomaly threshold:',
-            continuous_update=False
-        )
-    
-        def pause_interaction(b):
-            self.interaction_enabled = False
-            fig.update_layout(dragmode='pan')
-    
-        def resume_interaction(b):
-            self.interaction_enabled = True
-            fig.update_layout(dragmode='lasso')
-
-    
-        def update_threshold(change):
-            with output_threshold: 
-                output_threshold.clear_output(wait = True)
-                if print_flag: print("Update threshold")
-                self.threshold_ = change.new
-                if print_flag: print("Update anomalies threshold = ", self.threshold_)
-                update_anomalies()
-
-        #### Width
-        width_slider = FloatSlider(
-            value = 0.5,  
-            min   = 0.0,  
-            max   = 1.0,  
-            step  = 0.0001, 
-            description = 'Line width:',  
-            continuous_update = False  
-        )
-
-        def update_width(change):
-            with output_width:
-                try:
-                    output_width.clear_output(wait = True)
-                    if print_flag: 
-                        print("Change line width")
-                        print("Trace to update:", fig.data[1])  
-                    with fig.batch_update():
-                        fig.data[1].line.width = change.new  # Actualiza la opacidad de la línea
-                    if print_flag: print("ChangeD line width")
-                except Exception as e:
-                    print("Error updating line width:", e)
-                    
-
-
-        pause_button.on_click(pause_interaction)
-        resume_button.on_click(resume_interaction)
-    
-        threshold_slider.observe(update_threshold, 'value')
-    
-        #### 
-        width_slider.observe(update_width, names = 'value')
-        
-        #####
-        space = HTML("&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;") 
-    
-        vbox = VBox((output_tmp, output_button, output_anomaly, output_threshold, fig))
-        hbox = HBox((space, button, space, pause_button, space, resume_button, anomaly_button))
-    
-        # Centrar las dos cajas horizontalmente en el VBox
-
-        box_layout = widgets.Layout(display='flex',
-                    flex_flow='column',
-                    align_items='center',
-                    width='100%')
-
-        if self.anomaly_flag:
-            box = VBox((hbox,threshold_slider,width_slider, output_width, vbox), layout = box_layout)
-        else: 
-            box = VBox((hbox, width_slider, output_width, vbox), layout = box_layout)
-        box.add_class("layout")
-        plot_save(fig, self.w)
-    
-        display(box)
-
-
-# %% ../nbs/xai.ipynb 25
-def plot_save(fig, w):
-    image_bytes = pio.to_image(fig, format='png')
-    with open(f"../imgs/w={w}.png", 'wb') as f:
-        f.write(image_bytes)
-    
-
-# %% ../nbs/xai.ipynb 26
-def plot_initial_config(prjs, cluster_labels, anomaly_scores):
-    prjs_df = pd.DataFrame(prjs, columns = ['x1', 'x2'])
-    prjs_df['cluster'] = cluster_labels
-    prjs_df['anomaly_score'] = anomaly_scores
-    
-    cluster_colors_df = pd.DataFrame({'cluster': cluster_labels}).drop_duplicates()
-    cluster_colors_df['color'] = px.colors.qualitative.Set1[:len(cluster_colors_df)]
-    cluster_colors = dict(zip(cluster_colors_df['cluster'], cluster_colors_df['color']))
-    return prjs_df, cluster_colors
-
-# %% ../nbs/xai.ipynb 27
-def merge_overlapping_windows(windows):
-    if not windows:
-        return []
-
-    # Order
-    sorted_windows = sorted(windows, key=lambda x: x[0])
-
-    merged_windows = [sorted_windows[0]]
-
-    for window in sorted_windows[1:]:
-        if window[0] <= merged_windows[-1][1]:
-            # Merge!
-            merged_windows[-1] = (merged_windows[-1][0], max(window[1], merged_windows[-1][1]))
-        else:
-            merged_windows.append(window)
-
-    return merged_windows
-
-# %% ../nbs/xai.ipynb 29
-class InteractiveTSPlot: 
-    def __init__(
-        self, 
-        df, 
-        selected_indices, 
-        meaningful_features_subset_ids, 
-        w, 
-        stride=1, 
-        print_flag=False, 
-        num_points=10000, 
-        dateformat='%Y-%m-%d %H:%M:%S',
-        delta_x = 10, 
-        delta_y = 0.1  
-    ):
-        self.df = df
-        self.selected_indices = selected_indices
-        self.meaningful_features_subset_ids = meaningful_features_subset_ids
-        self.w = w
-        self.stride = stride
-        self.print_flag = print_flag
-        self.num_points = num_points
-        self.dateformat = dateformat
-        self.fig = go.FigureWidget()
-        self.buttons = []
-        self.print_flag = print_flag   
-    
-        self.delta_x = delta_x
-        self.delta_y = delta_y
-
-        self.window_ranges, self.n_windows, self.df_selected = get_df_selected(
-            self.df, self.selected_indices, self.w, self.stride
-        )
-        # Ensure the small possible number of windows to plot (like in R Shiny App)
-        self.window_ranges = merge_overlapping_windows(self.window_ranges)
-
-        #Num points no va bien...
-        #num_points = min(df_selected.shape[0], num_points)
-
-        if self.print_flag: 
-            print("windows: ", self.n_windows, self.window_ranges)
-            print("selected id: ", self.df_selected.index)
-            print("points: ", self.num_points)
-
-        self.df.index = self.df.index.astype(str)
-        self.fig = go.FigureWidget()
-        self.colors = [
-            f'rgb({np.random.randint(0, 256)}, {np.random.randint(0, 256)}, {np.random.randint(0, 256)})' 
-            for _ in range(self.n_windows)
-        ]
-
-        ##############################
-        # Outputs for debug printing #
-        ##############################
-        self.output_windows = Output()
-        self.output_move = Output()
-        self.output_delta_x = Output()
-        self.output_delta_y = Output()
-
-        
-        
-        
-
-# %% ../nbs/xai.ipynb 30
-def add_selected_features(self: InteractiveTSPlot):
-    # Add features time series
-    for feature_id in self.df.columns:
-        feature_pos = self.df.columns.get_loc(feature_id)
-        trace = go.Scatter(
-            #x=df.index[:num_points],
-            #y=df[feature_id][:num_points],
-            x = self.df.index,
-            y = self.df[feature_id],
-            mode='lines',
-            name=feature_id,
-            visible=feature_pos in self.meaningful_features_subset_ids,
-            text=self.df.index
-            #text=[f'{i}-{val}' for i, val in enumerate(df.index)]
-        )
-        self.fig.add_trace(trace)
-        
-InteractiveTSPlot.add_selected_features = add_selected_features
-
-# %% ../nbs/xai.ipynb 31
-def add_windows(self: InteractiveTSPlot):
-    for i, (start, end) in enumerate(self.window_ranges):
-        self.fig.add_shape(
-            type="rect",
-            x0=self.df.index[start],
-            x1=self.df.index[end],
-            y0= 0,
-            y1= 1,
-            yref = "paper",
-            fillcolor=self.colors[i], #"LightSalmon",
-            opacity=0.25,
-            layer="below",
-            line=dict(color=self.colors[i], width=1),
-            name = f"w_{i}"
-        )
-        with self.output_windows:
-            print("w[" + str( self.selected_indices[i] )+ "]="+str(self.df.index[start])+", "+str(self.df.index[end])+")")
-
-InteractiveTSPlot.add_windows = add_windows
-
-# %% ../nbs/xai.ipynb 32
-def setup_style(self: InteractiveTSPlot):
-    self.fig.update_layout(
-        title='Time Series with time window plot',
-        xaxis_title='Datetime',
-        yaxis_title='Value',
-        legend_title='Variables',
-        margin=dict(l=10, r=10, t=30, b=10),
-        xaxis=dict(
-            tickformat = '%d-' + self.dateformat,
-            #tickvals=list(range(len(df.index))),
-            #ticktext = [f'{i}-{val}' for i, val in enumerate(df.index)]
-            #grid_color = 'lightgray', zerolinecolor='black', title = 'x'
-        ),
-        #yaxis = dict(grid_color = 'lightgray', zerolinecolor='black', title = 'y'),
-        #plot_color = 'white',
-        paper_bgcolor='#f0f0f0'
-    )
-    self.fig.update_yaxes(fixedrange=True)
-
-InteractiveTSPlot.setup_style = setup_style
-
-# %% ../nbs/xai.ipynb 34
-def toggle_trace(self : InteractiveTSPlot, button : Button):
-    idx = button.description
-    trace = self.fig.data[self.df.columns.get_loc(idx)]
-    trace.visible = not trace.visible
-
-InteractiveTSPlot.toggle_trace = toggle_trace
-
-# %% ../nbs/xai.ipynb 35
-def set_features_buttons(self):
-    self.buttons = [
-        Button(
-            description=str(feature_id),
-            button_style='success' if self.df.columns.get_loc(feature_id) in self.meaningful_features_subset_ids else ''
-        ) 
-        for feature_id in self.df.columns
-    ]
-    for button in self.buttons:
-        button.on_click(self.toggle_trace)
-InteractiveTSPlot.set_features_buttons = set_features_buttons
-
-# %% ../nbs/xai.ipynb 36
-def move_left(self : InteractiveTSPlot, button : Button):
-    with self.output_move:
-        self.output_move.clear_output(wait=True)
-        start_date, end_date = self.fig.layout.xaxis.range
-        new_start_date = shift_datetime(start_date, self.delta_x, '-', self.dateformat, self.print_flag) 
-        new_end_date = shift_datetime(end_date, self.delta_x, '-', self.dateformat, self.print_flag) 
-        with self.fig.batch_update():
-            self.fig.layout.xaxis.range = [new_start_date, new_end_date]
-
-def move_right(self : InteractiveTSPlot, button : Button):
-    self.output_move.clear_output(wait=True)
-    with self.output_move:
-        start_date, end_date = self.fig.layout.xaxis.range
-        new_start_date = shift_datetime(start_date, self.delta_x, '+', self.dateformat, self.print_flag) 
-        new_end_date = shift_datetime(end_date, self.delta_x, '+', self.dateformat, self.print_flag) 
-        with self.fig.batch_update():
-            self.fig.layout.xaxis.range = [new_start_date, new_end_date]
-    
-def move_down(self: InteractiveTSPlot, button : Button):
-    with self.output_move:
-        self.output_move.clear_output(wait=True)
-        start_y, end_y = self.fig.layout.yaxis.range
-        with self.fig.batch_update():
-            self.ig.layout.yaxis.range = [start_y-self.delta_y, end_y-self.delta_y]
-def move_up(self: InteractiveTSPlot, button : Button):
-    with self.output_move:
-        self.output_move.clear_output(wait=True)
-        start_y, end_y = self.fig.layout.yaxis.range
-        with self.fig.batch_update():
-            self.fig.layout.yaxis.range = [start_y+self.delta_y, end_y+self.delta_y]
-
-InteractiveTSPlot.move_left = move_left
-InteractiveTSPlot.move_right = move_right
-InteractiveTSPlot.move_down = move_down
-InteractiveTSPlot.move_up = move_up
-
-# %% ../nbs/xai.ipynb 37
-def delta_x_bigger(self: InteractiveTSPlot):
-    with self.output_delta_x: 
-        self.output_delta_x.clear_output(wait = True)
-        if self.print_flag: print("Delta before", self.delta_x)
-        self.delta_x *= 10
-        if self.print_flag: print("delta_x:", self.delta_x)
-
-def delta_y_bigger(self: InteractiveTSPlot):
-    with self.output_delta_y: 
-        self.output_delta_y.clear_output(wait = True)
-        if self.print_flag: print("Delta before", self.delta_y)
-        self.delta_y *= 10
-        if self.print_flag: print("delta_y:", self.delta_y)
-
-def delta_x_lower(self:InteractiveTSPlot):
-    with self.output_delta_x:
-        self.output_delta_x.clear_output(wait = True)
-        if self.print_flag: print("Delta before", self.delta_x)
-        self.delta_x /= 10
-        if self.print_flag: print("delta_x:", self.delta_x)
-
-def delta_y_lower(self:InteractiveTSPlot):
-    with self.output_delta_y: 
-        self.output_delta_y.clear_output(wait = True)
-        print("Delta before", self.delta_y)
-        self.delta_y = self.delta_y * 10
-        print("delta_y:", self.delta_y)
-InteractiveTSPlot.delta_x_bigger = delta_x_bigger
-InteractiveTSPlot.delta_y_bigger = delta_y_bigger
-InteractiveTSPlot.delta_x_lower = delta_x_lower
-InteractiveTSPlot.delta_y_lower = delta_y_lower
-
-# %% ../nbs/xai.ipynb 38
-def add_movement_buttons(self: InteractiveTSPlot):
-    self.button_left = Button(description="←")
-    self.button_right = Button(description="→")
-    self.button_up = Button(description="↑")
-    self.button_down = Button(description="↓")
-    
-    self.button_step_x_up = Button(description="dx ↑")
-    self.button_step_x_down = Button(description="dx ↓")
-    self.button_step_y_up = Button(description="dy↑")
-    self.button_step_y_down = Button(description="dy↓")
-
-
-    # TODO: Arreglar que se pueda modificar el paso con el que se avanza. No se ve el output y no se modifica el valor
-    self.button_step_x_up.on_click(self.delta_x_bigger)
-    self.button_step_x_down.on_click(self.delta_x_lower)
-    self.button_step_y_up.on_click(self.delta_y_bigger)
-    self.button_step_y_down.on_click(self.delta_y_lower)
-
-    self.button_left.on_click(self.move_left)
-    self.button_right.on_click(self.move_right)
-    self.button_up.on_click(self.move_up)
-    self.button_down.on_click(self.move_down)
-
-InteractiveTSPlot.add_movement_buttons = add_movement_buttons
-
-# %% ../nbs/xai.ipynb 40
-def setup_boxes(self: InteractiveTSPlot):
-    self.steps_x = VBox([self.button_step_x_up, self.button_step_x_down])
-    self.steps_y = VBox([self.button_step_y_up, self.button_step_y_down])
-    arrow_buttons = HBox([self.button_left, self.button_right, self.button_up, self.button_down, self.steps_x, self.steps_y])
-    hbox_layout = widgets.Layout(display='flex', flex_flow='row wrap', align_items='flex-start')
-    hbox = HBox(self.buttons, layout=hbox_layout)
-    box_layout = widgets.Layout(
-        display='flex',
-        flex_flow='column',
-        align_items='center',
-        width='100%'
-    )
-    if self.print_flag:
-        self.box = VBox([hbox, arrow_buttons, self.output_move, self.output_delta_x, self.output_delta_y, self.fig, self.output_windows], layout=box_layout)
-    else: 
-        self.box = VBox([hbox, arrow_buttons, self.fig, self.output_windows], layout=box_layout)
-
-InteractiveTSPlot.setup_boxes = setup_boxes
-
-
-# %% ../nbs/xai.ipynb 41
-def initial_plot(self: InteractiveTSPlot):
-    self.add_selected_features()
-    self.add_windows()
-    self.setup_style()
-    self.set_features_buttons()
-    self.add_movement_buttons()
-    self.setup_boxes()
-InteractiveTSPlot.initial_plot = initial_plot
-
-# %% ../nbs/xai.ipynb 42
-def show(self : InteractiveTSPlot):
-    self.initial_plot()
-    display(self.box)
-InteractiveTSPlot.show = show

local_build_docker.sh

@@ -0,0 +1,14 @@
+# Inicializa un array vacío
+args=()
+
+# Lee el archivo .env línea por línea
+while IFS='=' read -r key value; do
+    if [[ $key != \#* && $key != '' ]]; then  # Excluye comentarios y líneas vacías
+        args+=(--build-arg "$key=$value")  # Agrega --build-arg y la variable como un elemento
+    fi
+done < .env
+
+echo "args: ${args[@]}"
+read -p "Press enter to continue"
+# Ejecuta docker build con los argumentos
+docker build "${args[@]}" . -t dvatshf

local_exec_docker.sh

@@ -4,10 +4,25 @@ args=()
 # Lee el archivo .env línea por línea
 while IFS='=' read -r key value; do
     if [[ $key != \#* && $key != '' ]]; then  # Excluye comentarios y líneas vacías
-        args+=(--build-arg "$key=$value")  # Agrega --build-arg y la variable como un elemento
+        args+=(-e "$key=$value")  # Agrega --build-arg y la variable como un elemento
     fi
 done < .env
 
 echo "args: ${args[@]}"
+#read -p "Press enter to continue"
+
+INTER=$1
+
+flags=()
+if((INTER == 1)); then
+    echo "INTERACTIVE"
+    flags+=(-it --entrypoint /bin/bash)
+fi
+
+flags+=("--gpus" "all")
+
+echo "${flags[@]}"
+
+#read -p "Press enter to continue"
 # Ejecuta docker build con los argumentos
-docker build "${args[@]}" . -t dvatshf
+docker run "${flags[@]}" "${args[@]}" -t dvatshf

r_shiny_app/global.R

@@ -52,7 +52,7 @@ if(torch$cuda$is_available()){
 
 # Python dependencies
 print("--> py dependences | Tsai")
-Sys.setenv(MPLCONFIGDIR = "/tmp/").
+Sys.setenv(MPLCONFIGDIR = "/tmp/")
 tsai_data = reticulate::import("tsai.data.all")
 print("--> py dependences | Wandb")
 wandb = reticulate::import("wandb")
@@ -85,9 +85,11 @@ DEFAULT_VALUES = list(metric_hdbscan = "euclidean",
                       path_alpha = 5/10,
                       point_alpha = 1/10,
                       point_size = 1)
+
 WANDB_ENTITY = Sys.getenv("WANDB_ENTITY")
 WANDB_PROJECT = Sys.getenv("WANDB_PROJECT")
 
+print("Wandb API Key -->", WANDB_API_KEY)
 
 ####################
 # HELPER FUNCTIONS #