Merge pull request #18 from florencejt/docs/usemnistexampledata

Docs/usemnistexampledata: Change all examples in docs to use MNIST data
florencejt · Jan 12, 2024 · 6b1b7af · 6b1b7af
2 parents 5bb530f + b1de556
commit 6b1b7af
Show file tree

Hide file tree

Showing 31 changed files with 3,988 additions and 1,196 deletions.
diff --git a/docs/_static/mnist_data/mnist1.csv b/docs/_static/mnist_data/mnist1.csv
diff --git a/docs/_static/mnist_data/mnist1_binary.csv b/docs/_static/mnist_data/mnist1_binary.csv
diff --git a/docs/_static/mnist_data/mnist1_regression.csv b/docs/_static/mnist_data/mnist1_regression.csv
diff --git a/docs/_static/mnist_data/mnist1_regression_test.csv b/docs/_static/mnist_data/mnist1_regression_test.csv
diff --git a/docs/_static/mnist_data/mnist2.csv b/docs/_static/mnist_data/mnist2.csv
diff --git a/docs/_static/mnist_data/mnist2_binary.csv b/docs/_static/mnist_data/mnist2_binary.csv
diff --git a/docs/_static/mnist_data/mnist2_regression.csv b/docs/_static/mnist_data/mnist2_regression.csv
diff --git a/docs/_static/mnist_data/mnist2_regression_test.csv b/docs/_static/mnist_data/mnist2_regression_test.csv
diff --git a/docs/conf.py b/docs/conf.py
@@ -23,6 +23,7 @@
 warnings.filterwarnings(
     "ignore", message="Checkpoint directory.*exists and is not empty."
 )
+warnings.filterwarnings("ignore", ".*samples in targets.*")
 
 project = "fusilli"
 copyright = "2023, Florence J Townend"

diff --git a/docs/examples/__init__.py b/docs/examples/__init__.py
@@ -1,90 +0,0 @@
-import pandas as pd
-from sklearn.datasets import make_classification, make_regression
-
-
-def generate_sklearn_simulated_data(
-        prediction_task, num_samples, num_tab1_features, num_tab2_features, external=False
-):
-    """
-    Generate simulated data for all modalities, adds a prediction label and returns the params dictionary
-
-    Parameters
-    ----------
-    prediction_task : str
-        The type of prediction to be performed. This is either ``regression``, ``binary``, or ``classification``.
-    num_samples : int
-        Number of samples to generate
-    num_tab1_features : int
-        Number of features to generate for tabular1 data
-    num_tab2_features : int
-        Number of features to generate for tabular2 data
-
-    Returns
-    -------
-    params : dict
-        Dictionary of parameters with the paths to the simulated data added
-    """
-    if external:
-        tabular1_path = "../../../fusilli/utils/simulated_data/external_tabular1data.csv"
-        tabular2_path = "../../../fusilli/utils/simulated_data/external_tabular2data.csv"
-    else:
-        tabular1_path = "../../../fusilli/utils/simulated_data/tabular1data.csv"
-        tabular2_path = "../../../fusilli/utils/simulated_data/tabular2data.csv"
-
-    if prediction_task == "binary":
-        # Creating a simulated feature matrix and output vector with 100 samples
-        all_tab_features, labels = make_classification(
-            n_samples=num_samples,
-            n_features=num_tab1_features + num_tab2_features,  # taking  features
-            n_informative=(num_tab1_features + num_tab2_features)
-                          // 3,  # features that predict the output's classes
-            n_classes=2,  # three output classes
-            weights=None,  # equal number of samples per class)
-            flip_y=0.1,  # flip 10% of the labels
-        )
-    elif prediction_task == "multiclass":
-        num_classes = 3
-        all_tab_features, labels = make_classification(
-            n_samples=num_samples,
-            n_features=num_tab1_features + num_tab2_features,  # taking  features
-            n_informative=(num_tab1_features + num_tab2_features)
-                          // 2,  # features that predict the output's classes
-            n_classes=num_classes,  # three output classes
-            weights=None,  # equal number of samples per class)
-            flip_y=0.1,  # flip 10% of the labels
-        )
-    elif prediction_task == "regression":
-        all_tab_features, labels = make_regression(
-            n_samples=num_samples,
-            n_features=num_tab1_features + num_tab2_features,  # taking  features
-            n_informative=(num_tab1_features + num_tab2_features)
-                          // 2,  # features that predict the output's classes
-            noise=3,
-            effective_rank=3,
-        )
-    else:
-        raise ValueError(
-            f"pred_type must be one of 'binary', 'multiclass' or 'regression', not {prediction_task}"
-        )
-
-    tabular1_data = pd.DataFrame()
-    tabular1_data["ID"] = [f"{i}" for i in range(num_samples)]
-    for i in range(num_tab1_features):
-        feature_name = f"feature{i + 1}"
-        tabular1_data[feature_name] = all_tab_features[:, i]
-    tabular1_data.set_index("ID", inplace=True)
-    tabular1_data["prediction_label"] = labels
-
-    tabular2_data = pd.DataFrame()
-    tabular2_data["ID"] = [f"{i}" for i in range(num_samples)]
-    for i in range(num_tab2_features):
-        feature_name = f"feature{i + 1}"
-        tabular2_data[feature_name] = all_tab_features[:, num_tab1_features + i]
-    tabular2_data.set_index("ID", inplace=True)
-    tabular2_data["prediction_label"] = labels
-
-    # save to csv and pt
-    tabular1_data.to_csv(tabular1_path)
-    tabular2_data.to_csv(tabular2_path)
-
-    return tabular1_path, tabular2_path

diff --git a/docs/examples/customising_behaviour/loss_figures/losses/AttentionWeightedGNN.png b/docs/examples/customising_behaviour/loss_figures/losses/AttentionWeightedGNN.png
diff --git a/...examples/customising_behaviour/loss_figures/losses/ConcatTabularFeatureMaps.png b/...examples/customising_behaviour/loss_figures/losses/ConcatTabularFeatureMaps.png
diff --git a/.../examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/hparams.yaml b/.../examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/hparams.yaml
@@ -0,0 +1 @@
+{}
diff --git a/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv b/docs/examples/customising_behaviour/loss_logs/modify_layers/AttentionWeightedGNN/metrics.csv
@@ -0,0 +1,12 @@
+epoch,val_loss,MAE_val,R2_val,step,train_loss,MAE_train,R2_train
+0,91613.78125,255.58047485351562,-10884.7041015625,0,,,
+0,,,,0,126736.65625,283.7513427734375,-14838.0244140625
+1,22806.869140625,133.0036163330078,-2708.950927734375,1,,,
+1,,,,1,95430.828125,245.3120574951172,-11172.5654296875
+2,2364.317138671875,40.63412857055664,-279.9321594238281,2,,,
+2,,,,2,39681.44921875,155.34410095214844,-4645.12158203125
+3,948.3421630859375,24.115896224975586,-111.68362426757812,3,,,
+3,,,,3,24457.408203125,121.0082778930664,-2862.607666015625
+4,516.4700927734375,18.50086212158203,-60.367855072021484,4,,,
+4,,,,4,13347.4072265625,91.87945556640625,-1561.7877197265625
+5,516.4700927734375,18.50086212158203,-60.367855072021484,5,,,
diff --git a/...mples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/hparams.yaml b/...mples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/hparams.yaml
@@ -0,0 +1 @@
+{}
diff --git a/...amples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv b/...amples/customising_behaviour/loss_logs/modify_layers/ConcatTabularFeatureMaps/metrics.csv
@@ -0,0 +1,12 @@
+epoch,val_loss,MAE_val,R2_val,step,train_loss,MAE_train,R2_train
+0,4.192819595336914,1.7022111415863037,0.5379555225372314,49,,,
+0,,,,49,8.381141662597656,2.3306796550750732,-0.24445590376853943
+1,3.2308895587921143,1.4975028038024902,0.6439592838287354,99,,,
+1,,,,99,3.6040618419647217,1.4797532558441162,0.46048277616500854
+2,2.6154050827026367,1.2608485221862793,0.711785078048706,149,,,
+2,,,,149,2.725576877593994,1.2617511749267578,0.5758683681488037
+3,2.830514430999756,1.331453561782837,0.6880801916122437,199,,,
+3,,,,199,1.5875434875488281,0.9392735958099365,0.7461423277854919
+4,3.048844337463379,1.3958659172058105,0.6640203595161438,249,,,
+4,,,,249,1.254799246788025,0.8462849259376526,0.7862377762794495
+5,3.048844337463379,1.3958659172058105,0.6640203595161438,250,,,
diff --git a/docs/examples/customising_behaviour/plot_modify_layer_sizes.py b/docs/examples/customising_behaviour/plot_modify_layer_sizes.py
@@ -18,7 +18,7 @@
 # Setting up the experiment
 # -------------------------
 #
-# First, we will set up the experiment by importing the necessary packages, creating the simulated data, and setting the parameters for the experiment.
+# First, we will set up the experiment by importing the necessary packages, specifying MNIST data paths, and setting the parameters for the experiment.
 #
 # For a more detailed explanation of this process, please see the example tutorials.
 #
@@ -29,9 +29,7 @@
 import torch.nn as nn
 from torch_geometric.nn import GCNConv, ChebConv
 
-from docs.examples import generate_sklearn_simulated_data
 from fusilli.data import prepare_fusion_data
-from fusilli.eval import RealsVsPreds
 from fusilli.train import train_and_save_models
 
 from fusilli.fusionmodels.tabularfusion.attention_weighted_GNN import AttentionWeightedGNN
@@ -54,14 +52,9 @@
     # remove dir
     os.rmdir(os.path.join(output_paths["losses"], dir))
 
-tabular1_path, tabular2_path = generate_sklearn_simulated_data(prediction_task,
-                                                               num_samples=500,
-                                                               num_tab1_features=10,
-                                                               num_tab2_features=20)
-
 data_paths = {
-    "tabular1": tabular1_path,
-    "tabular2": tabular2_path,
+    "tabular1": "../../_static/mnist_data/mnist1_regression.csv",
+    "tabular2": "../../_static/mnist_data/mnist2_regression.csv",
     "image": "",
 }
 
@@ -115,7 +108,7 @@
 layer_mods = {
     "AttentionWeightedGNN": {
         "graph_conv_layers": nn.Sequential(
-            ChebConv(20, 50, K=3),
+            ChebConv(392, 50, K=3),
             ChebConv(50, 100, K=3),
             ChebConv(100, 130, K=3),
         ),
@@ -127,19 +120,19 @@
         "AttentionWeightingMLPInstance.weighting_layers": nn.ModuleDict(
             {
                 "Layer 1": nn.Sequential(
-                    nn.Linear(30, 100),
+                    nn.Linear(784, 500),
                     nn.ReLU()),
                 "Layer 2": nn.Sequential(
-                    nn.Linear(100, 75),
+                    nn.Linear(500, 128),
                     nn.ReLU()),
                 "Layer 3": nn.Sequential(
-                    nn.Linear(75, 75),
+                    nn.Linear(128, 128),
                     nn.ReLU()),
                 "Layer 4": nn.Sequential(
-                    nn.Linear(75, 100),
+                    nn.Linear(128, 500),
                     nn.ReLU()),
                 "Layer 5": nn.Sequential(
-                    nn.Linear(100, 30),
+                    nn.Linear(500, 784),
                     nn.ReLU()),
             }
         )},
@@ -211,7 +204,7 @@
 layer_mods = {
     "AttentionWeightedGraphMaker": {
         "AttentionWeightingMLPInstance.weighting_layers": nn.Sequential(
-            nn.Linear(30, 75),
+            nn.Linear(392, 75),
             nn.ReLU(),
             nn.Linear(75, 75),
             nn.ReLU(),
@@ -268,37 +261,37 @@
         "mod1_layers": nn.ModuleDict(
             {
                 "layer 1": nn.Sequential(
-                    nn.Linear(10, 32),
+                    nn.Linear(392, 300),
                     nn.ReLU(),
                 ),
                 "layer 2": nn.Sequential(
-                    nn.Linear(32, 66),
+                    nn.Linear(300, 128),
                     nn.ReLU(),
                 ),
                 "layer 3": nn.Sequential(
-                    nn.Linear(66, 128),
+                    nn.Linear(128, 128),
                     nn.ReLU(),
                 ),
             }
         ),
         "mod2_layers": nn.ModuleDict(
             {
                 "layer 1": nn.Sequential(
-                    nn.Linear(20, 45),
+                    nn.Linear(392, 300),
                     nn.ReLU(),
                 ),
                 "layer 2": nn.Sequential(
-                    nn.Linear(45, 70),
+                    nn.Linear(300, 128),
                     nn.ReLU(),
                 ),
                 "layer 3": nn.Sequential(
-                    nn.Linear(70, 100),
+                    nn.Linear(128, 100),
                     nn.ReLU(),
                 ),
             }
         ),
         "fused_layers": nn.Sequential(
-            nn.Linear(30, 150),
+            nn.Linear(25, 150),
             nn.ReLU(),
             nn.Linear(150, 75),
             nn.ReLU(),

diff --git a/...les/model_comparison/loss_logs/two_models_traintest/ConcatTabularFeatureMaps/hparams.yaml b/...les/model_comparison/loss_logs/two_models_traintest/ConcatTabularFeatureMaps/hparams.yaml
@@ -0,0 +1 @@
+{}
diff --git a/...ples/model_comparison/loss_logs/two_models_traintest/ConcatTabularFeatureMaps/metrics.csv b/...ples/model_comparison/loss_logs/two_models_traintest/ConcatTabularFeatureMaps/metrics.csv
@@ -0,0 +1,130 @@
+MAE_val,epoch,val_loss,R2_val,step,R2_train,train_loss,MAE_train
+2.550821542739868,0,9.993765830993652,-0.36750197410583496,7,,,
+,0,,,7,-0.9941763877868652,16.622907638549805,3.3130204677581787
+2.0250279903411865,1,6.120434284210205,0.1625072956085205,15,,,
+,1,,,15,0.14741721749305725,7.222578048706055,2.1738603115081787
+2.1401681900024414,2,7.6228156089782715,-0.04307210445404053,23,,,
+,2,,,23,0.3598036766052246,5.378986835479736,1.850309133529663
+1.7970805168151855,3,5.474188804626465,0.25093650817871094,31,,,
+,3,,,31,0.46779435873031616,4.45926570892334,1.6522819995880127
+1.7589600086212158,4,5.531983375549316,0.24302823841571808,39,,,
+,4,,,39,0.549390971660614,3.7244315147399902,1.50660240650177
+1.7531583309173584,5,5.516550540924072,0.24513985216617584,47,,,
+,5,,,47,0.6313987970352173,3.1030783653259277,1.3298112154006958
+1.8356657028198242,6,6.117806434631348,0.1628667712211609,55,,,
+,6,,,55,0.7357838749885559,2.1222023963928223,1.1111221313476562
+1.6618714332580566,7,5.2861833572387695,0.27666234970092773,63,,,
+,7,,,63,0.7709552049636841,1.9139409065246582,1.0187078714370728
+1.57106614112854,8,4.7738189697265625,0.3467719554901123,71,,,
+,8,,,71,0.7927483320236206,1.7200523614883423,0.9630319476127625
+1.604772925376892,9,4.912045001983643,0.3278576731681824,79,,,
+,9,,,79,0.8458226919174194,1.3106739521026611,0.8364636898040771
+1.6100950241088867,10,4.99435567855835,0.316594660282135,87,,,
+,10,,,87,0.8695825934410095,1.0703457593917847,0.7448607683181763
+1.5111275911331177,11,4.433798789978027,0.39329880475997925,95,,,
+,11,,,95,0.8898072838783264,0.9113877415657043,0.6880857348442078
+1.5012693405151367,12,4.484768867492676,0.38632428646087646,103,,,
+,12,,,103,0.9058310985565186,0.7781545519828796,0.623124361038208
+1.4845701456069946,13,4.483181476593018,0.386541485786438,111,,,
+,13,,,111,0.9015885591506958,0.8185599446296692,0.6399004459381104
+1.4515013694763184,14,4.19098424911499,0.42652440071105957,119,,,
+,14,,,119,0.917405366897583,0.7098179459571838,0.5883429646492004
+1.6063990592956543,15,5.107571601867676,0.30110275745391846,127,,,
+,15,,,127,0.9220234751701355,0.6577357053756714,0.5817375183105469
+1.6577519178390503,16,5.300245761871338,0.274738073348999,135,,,
+,16,,,135,0.910961925983429,0.750971257686615,0.6317367553710938
+1.6055717468261719,17,5.1569318771362305,0.29434847831726074,143,,,
+,17,,,143,0.9021233916282654,0.8221578598022461,0.6475740075111389
+1.639248013496399,18,5.171427249908447,0.29236501455307007,151,,,
+,18,,,151,0.900653064250946,0.8402024507522583,0.6508421897888184
+1.5495505332946777,19,4.559011459350586,0.3761652708053589,159,,,
+,19,,,159,0.908098042011261,0.7756197452545166,0.6134839653968811
+1.5707274675369263,20,4.734753608703613,0.35211747884750366,167,,,
+,20,,,167,0.924924910068512,0.6461987495422363,0.5735353827476501
+1.4228588342666626,21,3.9855732917785645,0.45463207364082336,175,,,
+,21,,,175,0.8851704001426697,0.9396422505378723,0.6774986386299133
+1.5047160387039185,22,4.698852062225342,0.35703009366989136,183,,,
+,22,,,183,0.9096055626869202,0.7635362148284912,0.6242097616195679
+1.3928208351135254,23,4.0358099937438965,0.4477577209472656,191,,,
+,23,,,191,0.9363532662391663,0.5386157631874084,0.5349072217941284
+1.431113600730896,24,4.470632076263428,0.38825875520706177,199,,,
+,24,,,199,0.9415258169174194,0.4951440393924713,0.48046642541885376
+1.4176338911056519,25,4.323647499084473,0.4083714485168457,207,,,
+,25,,,207,0.9421520829200745,0.4700969457626343,0.47758549451828003
+1.3952497243881226,26,4.078487873077393,0.44191786646842957,215,,,
+,26,,,215,0.9555037021636963,0.3723544776439667,0.41501185297966003
+1.3771188259124756,27,3.975924491882324,0.45595234632492065,223,,,
+,27,,,223,0.9446684718132019,0.47763383388519287,0.46678513288497925
+1.39987313747406,28,4.1419172286987305,0.43323853611946106,231,,,
+,28,,,231,0.950677752494812,0.4249856173992157,0.46403563022613525
+1.396775245666504,29,4.147250652313232,0.4325087368488312,239,,,
+,29,,,239,0.9434041976928711,0.48246464133262634,0.4782574772834778
+1.390718936920166,30,4.0993194580078125,0.43906742334365845,247,,,
+,30,,,247,0.9475470185279846,0.4307110905647278,0.45598751306533813
+1.3702670335769653,31,3.924558162689209,0.4629810154438019,255,,,
+,31,,,255,0.9597123265266418,0.33705028891563416,0.40675410628318787
+1.4085280895233154,32,4.261821269989014,0.41683149337768555,263,,,
+,32,,,263,0.9405664205551147,0.4967845678329468,0.47750386595726013
+1.3695627450942993,33,3.9851067066192627,0.4546957314014435,271,,,
+,33,,,271,0.9426913857460022,0.46704617142677307,0.47678470611572266
+1.4094480276107788,34,3.998617649078369,0.45284709334373474,279,,,
+,34,,,279,0.9507781863212585,0.41333433985710144,0.4429933428764343
+1.3696682453155518,35,3.881700038909912,0.4688454568386078,287,,,
+,35,,,287,0.939416766166687,0.4943542778491974,0.49081888794898987
+1.4207769632339478,36,4.0211310386657715,0.4497663974761963,295,,,
+,36,,,295,0.9628061056137085,0.3204095661640167,0.3762396574020386
+1.3708957433700562,37,3.8153915405273438,0.4779188930988312,303,,,
+,37,,,303,0.945135235786438,0.4457513093948364,0.4563809335231781
+1.4144178628921509,38,4.181662559509277,0.4277999997138977,311,,,
+,38,,,311,0.953737199306488,0.37626779079437256,0.41615381836891174
+1.4202336072921753,39,4.486833095550537,0.38604187965393066,319,,,
+,39,,,319,0.9369199872016907,0.5130748152732849,0.48724305629730225
+1.341239094734192,40,3.9252257347106934,0.4628896713256836,327,,,
+,40,,,327,0.9515608549118042,0.40434104204177856,0.4478156864643097
+1.539413332939148,41,4.673248291015625,0.36053353548049927,335,,,
+,41,,,335,0.9387563467025757,0.5167527794837952,0.5156168937683105
+1.7450042963027954,42,5.426409721374512,0.2574743628501892,343,,,
+,42,,,343,0.8893561959266663,0.9203564524650574,0.6920171976089478
+1.5992671251296997,43,4.80446195602417,0.3425789475440979,351,,,
+,43,,,351,0.90372234582901,0.773030161857605,0.6137067675590515
+1.4908099174499512,44,4.300416946411133,0.41155022382736206,359,,,
+,44,,,359,0.920629620552063,0.6477848887443542,0.574256956577301
+1.44012451171875,45,4.305934429168701,0.4107951521873474,367,,,
+,45,,,367,0.9128478169441223,0.7299042344093323,0.6047486066818237
+1.4437092542648315,46,4.356184005737305,0.4039192795753479,375,,,
+,46,,,375,0.9185203909873962,0.6919479966163635,0.5823092460632324
+1.3632363080978394,47,4.0035600662231445,0.4521707594394684,383,,,
+,47,,,383,0.9386501908302307,0.5217953324317932,0.5004262328147888
+1.3210381269454956,48,3.677823543548584,0.49674296379089355,391,,,
+,48,,,391,0.9318889379501343,0.5586847066879272,0.5229012370109558
+1.3599282503128052,49,4.0863728523254395,0.4408389925956726,399,,,
+,49,,,399,0.9411574602127075,0.480117529630661,0.45792442560195923
+1.330924391746521,50,3.81209397315979,0.4783701002597809,407,,,
+,50,,,407,0.9523176550865173,0.38065025210380554,0.42401397228240967
+1.332220435142517,51,3.883242130279541,0.46863454580307007,415,,,
+,51,,,415,0.9544154405593872,0.3866163492202759,0.4231767952442169
+1.3906828165054321,52,4.229849815368652,0.4212062954902649,423,,,
+,52,,,423,0.9538908004760742,0.3787587583065033,0.42101413011550903
+1.3811572790145874,53,3.968116044998169,0.45702075958251953,431,,,
+,53,,,431,0.9562690258026123,0.3682621121406555,0.40828174352645874
+1.366807460784912,54,4.064504146575928,0.4438314437866211,439,,,
+,54,,,439,0.955906093120575,0.365860253572464,0.421358585357666
+1.3128173351287842,55,3.7178304195404053,0.49126872420310974,447,,,
+,55,,,447,0.9617348909378052,0.3125576674938202,0.399527370929718
+1.3332505226135254,56,3.8579626083374023,0.4720936119556427,455,,,
+,56,,,455,0.9516554474830627,0.39683592319488525,0.42932966351509094
+1.3601473569869995,57,4.019503593444824,0.44998905062675476,463,,,
+,57,,,463,0.9577350616455078,0.3561760187149048,0.3812394440174103
+1.3970615863800049,58,4.052127838134766,0.44552499055862427,471,,,
+,58,,,471,0.9404559135437012,0.4958237111568451,0.46953076124191284
+1.614027500152588,59,4.767022609710693,0.3477018475532532,479,,,
+,59,,,479,0.9305849671363831,0.5771317481994629,0.541130781173706
+1.4933671951293945,60,4.511728286743164,0.38263529539108276,487,,,
+,60,,,487,0.9263445138931274,0.5848267078399658,0.5564913153648376
+1.3761061429977417,61,3.990039110183716,0.4540208578109741,495,,,
+,61,,,495,0.9608601331710815,0.31816357374191284,0.3896094262599945
+1.4019064903259277,62,4.246546268463135,0.4189215302467346,503,,,
+,62,,,503,0.9589810371398926,0.34225142002105713,0.40126940608024597
+1.441614031791687,63,4.2292351722717285,0.42129039764404297,511,,,
+,63,,,511,0.9644243717193604,0.29825612902641296,0.37975960969924927
+1.441614031791687,64,4.2292351722717285,0.42129039764404297,512,,,
diff --git a/docs/examples/model_comparison/loss_logs/two_models_traintest/Tabular1Unimodal/hparams.yaml b/docs/examples/model_comparison/loss_logs/two_models_traintest/Tabular1Unimodal/hparams.yaml
@@ -0,0 +1 @@
+{}