[USGS-R#45] add replicates to baseline model

2a_model/src/models/0_baseline_LSTM/Snakefile

@@ -7,9 +7,7 @@ import os
 import tensorflow as tf
 import numpy as np
 import pandas as pd
-import inspect
 
-import river_dl
 from river_dl.preproc_utils import asRunConfig
 from river_dl.preproc_utils import prep_all_data
 from river_dl.evaluate import combined_metrics
@@ -24,9 +22,10 @@ loss_function = lf.multitask_rmse(config['lambdas'])
 
 rule all:
     input:
-        expand("{outdir}/{metric_type}_metrics.csv",
+        expand("{outdir}/rep_{rep}/{metric_type}_metrics.csv",
                 outdir=out_dir,
                 metric_type=['overall', 'reach'],
+                rep=list(range(config['num_replicates'])),
         )
 
 
@@ -131,7 +130,7 @@ def filter_predictions(all_preds_file, partition, out_file):
         df_preds_val_sites = df_preds[df_preds.site_id.isin(config['validation_sites'])] 
 
 
-        if partition == "train":
+        if partition == "trn":
             df_preds_filt = df_preds_trn_sites[(df_preds_trn_sites.date >= config['train_start_date'][0]) &
                                                (df_preds_trn_sites.date < config['train_end_date'][0])]
         elif partition == "val":

2a_model/src/models/0_baseline_LSTM/analyze_states.smk

@@ -1,29 +1,34 @@
+code_dir = '../river-dl'
+import sys
+sys.path.insert(0, code_dir)
+# if using river_dl installed with pip this is not needed
+
 from model import LSTMModelStates
 from river_dl.postproc_utils import prepped_array_to_df
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 
-code_dir = '../river-dl'
-# if using river_dl installed with pip this is not needed
-import sys
-sys.path.insert(0, code_dir)
 
 out_dir = "../../../out/models/0_baseline_LSTM/analyze_states"
 in_dir = "../../../out/models/0_baseline_LSTM"
 
 
 def get_site_ids():
-    df = pd.read_csv(f"{in_dir}/reach_metrics.csv", dtype={"site_id": str})
+    df = pd.read_csv(f"{in_dir}/rep_0/reach_metrics.csv", dtype={"site_id": str})
     return df.site_id.unique()
 
 
 rule all:
     input:
-        expand("{outdir}/states_{trained_or_random}_{site_id}.png",
+        expand("{outdir}/rep_{rep}/states_{trained_or_random}_{site_id}.png",
                outdir=out_dir,
+               rep=list(range(6)),
                trained_or_random = ["trained", "random"],
-               site_id = get_site_ids())
+               site_id = get_site_ids()),
+        expand("{outdir}/rep_{rep}/output_weights.jpg",
+               outdir=out_dir,
+               rep=list(range(6))),
 
 
 model = LSTMModelStates(
@@ -36,10 +41,10 @@ model = LSTMModelStates(
 
 rule write_states:
     input:
-        f"{in_dir}/prepped.npz",
-        f"{in_dir}/train_weights/",
+        f"{in_dir}/rep_{{rep}}/prepped.npz",
+        f"{in_dir}/rep_{{rep}}/train_weights/",
     output:
-        "{outdir}/states_{trained_or_random}.csv"
+        "{outdir}/rep_{rep}/states_{trained_or_random}.csv"
     run:
         data = np.load(input[0], allow_pickle=True)
         if wildcards.trained_or_random == "trained":
@@ -62,7 +67,41 @@ rule plot_states:
         df_site = df.query(f"site_id == '{wildcards.site_id}'")
         del df_site["site_id"]
         df_site = df_site.set_index("date")
-        df_site.plot(subplots=True, figsize=(8,10))
+        axs = df_site.plot(subplots=True, figsize=(8,10))
+        for ax in axs.flatten():
+            ax.legend(loc = "upper left")
+        plt.suptitle(wildcards.site_id)
         plt.tight_layout()
         plt.savefig(output[0])
 
+
+rule plot_output_weights:
+    input:
+        f"{in_dir}/rep_{{rep}}/prepped.npz",
+        f"{in_dir}/rep_{{rep}}/train_weights/",
+    output:
+        "{outdir}/rep_{rep}/output_weights.jpg"
+    run:
+        data = np.load(input[0], allow_pickle=True)
+        m = LSTMModelStates(
+            config['hidden_size'],
+            recurrent_dropout=config['recurrent_dropout'],
+            dropout=config['dropout'],
+            num_tasks=len(config['y_vars'])
+        )
+        m.load_weights(input[1] + "/")
+        m(data['x_val'])
+        w = m.weights
+        ax = plt.imshow(w[3].numpy())
+        fig = plt.gcf()
+        cbar = fig.colorbar(ax)
+        cbar.set_label('weight value')
+        ax = plt.gca()
+        ax.set_yticks(list(range(10)))
+        ax.set_yticklabels(f"h{i}" for i in range(10))
+        ax.set_ylabel('hidden state')
+        ax.set_xticks(list(range(3)))
+        ax.set_xticklabels(["DO_max", "DO_mean", "DO_min"], rotation=90)
+        ax.set_xlabel('output variable')
+        plt.tight_layout()
+        plt.savefig(output[0], bbox_inches='tight')

2a_model/src/models/0_baseline_LSTM/config.yml

@@ -6,6 +6,7 @@ x_vars: ['seg_ccov', 'seg_rain', 'seg_slope', 'seg_tave_air', 'hru_slope', 'hru_
 
 
 seed: False #random seed for training False==No seed, otherwise specify the seed
+num_replicates: 6
 
 y_vars: ['do_min', 'do_mean', 'do_max']