diff --git a/notebooks/frrd-80/README.md b/notebooks/frrd-80/README.md
deleted file mode 100644
index efeb715..0000000
--- a/notebooks/frrd-80/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Jupyter Notebooks
-
-## FRRD-80: Visualization of FixMatch
-
-This simply shows the decision boundaries constructed by the FixMatch model.
diff --git a/notebooks/frrd-80/.gitignore b/notebooks/frrd-81/.gitignore
similarity index 100%
rename from notebooks/frrd-80/.gitignore
rename to notebooks/frrd-81/.gitignore
diff --git a/notebooks/frrd-81/README.md b/notebooks/frrd-81/README.md
new file mode 100644
index 0000000..93f509c
--- /dev/null
+++ b/notebooks/frrd-81/README.md
@@ -0,0 +1,20 @@
+# Jupyter Notebooks
+
+The notebook may not render Plotly on GitHub.
+View it on: https://nbviewer.org/
+
+## FRRD-80/81: Visualization of FixMatch
+
+This simply shows the embeddings of the FixMatch model.
+The embeddings are generated by the model and then visualized using LDA.
+
+We found that the model transfer doesn't necessarily equate to a well separated
+embedding space. This is likely due to the fact that the model is trained on
+too small of a dataset.
+
+We attribute much of performance degradation to our contextual shift when
+we chose our backbone model. We chose a model that was trained on ImageNet,
+which in retrospect, has a large contextual shift from our dataset.
+
+However, it's not all bad, we reinforced the idea that this shift is a real
+problem and that we need to address it in our future work.
diff --git a/notebooks/frrd-80/epoch=0-step=150.ckpt b/notebooks/frrd-81/epoch=0-step=150.ckpt
similarity index 100%
rename from notebooks/frrd-80/epoch=0-step=150.ckpt
rename to notebooks/frrd-81/epoch=0-step=150.ckpt
diff --git a/notebooks/frrd-80/epoch=1-step=300.ckpt b/notebooks/frrd-81/epoch=1-step=300.ckpt
similarity index 100%
rename from notebooks/frrd-80/epoch=1-step=300.ckpt
rename to notebooks/frrd-81/epoch=1-step=300.ckpt
diff --git a/notebooks/frrd-80/epoch=2-step=450.ckpt b/notebooks/frrd-81/epoch=2-step=450.ckpt
similarity index 100%
rename from notebooks/frrd-80/epoch=2-step=450.ckpt
rename to notebooks/frrd-81/epoch=2-step=450.ckpt
diff --git a/notebooks/frrd-80/epoch=3-step=600.ckpt b/notebooks/frrd-81/epoch=3-step=600.ckpt
similarity index 100%
rename from notebooks/frrd-80/epoch=3-step=600.ckpt
rename to notebooks/frrd-81/epoch=3-step=600.ckpt
diff --git a/notebooks/frrd-80/epoch=4-step=750.ckpt b/notebooks/frrd-81/epoch=4-step=750.ckpt
similarity index 100%
rename from notebooks/frrd-80/epoch=4-step=750.ckpt
rename to notebooks/frrd-81/epoch=4-step=750.ckpt
diff --git a/notebooks/frrd-80/epoch=5-step=900.ckpt b/notebooks/frrd-81/epoch=5-step=900.ckpt
similarity index 100%
rename from notebooks/frrd-80/epoch=5-step=900.ckpt
rename to notebooks/frrd-81/epoch=5-step=900.ckpt
diff --git a/notebooks/frrd-80/nb.ipynb b/notebooks/frrd-81/nb.ipynb
similarity index 73%
rename from notebooks/frrd-80/nb.ipynb
rename to notebooks/frrd-81/nb.ipynb
index 750045b..2fcd407 100644
--- a/notebooks/frrd-80/nb.ipynb
+++ b/notebooks/frrd-81/nb.ipynb
@@ -2,15 +2,6 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
- "id": "766f20d6021303d8",
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-06-04T04:53:05.673267600Z",
- "start_time": "2024-06-04T04:53:05.663612600Z"
- }
- },
"outputs": [
{
"name": "stdout",
@@ -27,387 +18,244 @@
],
"source": [
"from __future__ import annotations\n",
+ "\n",
"import os\n",
"from pathlib import Path\n",
"\n",
+ "import pandas as pd\n",
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "import torch\n",
+ "from scipy.spatial import ConvexHull\n",
+ "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
+ "\n",
+ "from frdc.models.efficientnetb1 import EfficientNetB1FixMatchModule\n",
+ "from frdc.train.utils import preprocess\n",
+ "from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n",
+ "\n",
"# disable gpu\n",
"# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
+ "torch.set_grad_enabled(False)\n",
"\n",
"curr_dir = Path(os.path.abspath(\"\"))\n",
"model_paths = list(curr_dir.glob(\"*.ckpt\"))\n",
"for model_path in model_paths:\n",
" print(model_path.name)"
- ]
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-06-04T09:40:03.558651700Z",
+ "start_time": "2024-06-04T09:39:49.665390Z"
+ }
+ },
+ "id": "23149afcfcfc144e",
+ "execution_count": 1
},
{
"cell_type": "code",
"outputs": [],
"source": [
- "from pathlib import Path\n",
- "\n",
- "import numpy as np\n",
- "import plotly.express as px\n",
- "import plotly.graph_objects as go\n",
- "from scipy.spatial import ConvexHull\n",
- "from sklearn.preprocessing import OrdinalEncoder\n",
- "from sklearn.svm import SVC\n",
- "\n",
- "\n",
- "def get_df_lda(model_paths: list[str | Path]):\n",
- " from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n",
- " import torch\n",
- "\n",
- " ds = FRDCDatasetStaticEval(\n",
- " \"chestnut_nature_park\",\n",
- " \"20201218\",\n",
- " None,\n",
- " transform=val_preprocess(255),\n",
- " )\n",
- " xs_raw, ys_raw = list(zip(*ds))\n",
- " xs_raw = torch.stack(xs_raw)\n",
- "\n",
- " def extract_model(model_path):\n",
- " m = EfficientNetB1FixMatchModule.load_from_checkpoint(\n",
- " model_path\n",
- " ).eval()\n",
- " m: EfficientNetB1FixMatchModule\n",
- " m_eff = m.eff\n",
- " m_fc = m.fc[0]\n",
- " return m_eff, m_fc, m.x_scaler, m.y_encoder\n",
- "\n",
- " def extract_lda(xs_eff, xs_fc, ys, lda_eff, lda_fc, n_components=2):\n",
- " if lda_eff is None:\n",
- " lda_eff = LinearDiscriminantAnalysis(n_components=n_components)\n",
- " lda_eff.fit(xs_eff, ys)\n",
- " if lda_fc is None:\n",
- " lda_fc = LinearDiscriminantAnalysis(n_components=n_components)\n",
- " lda_fc.fit(xs_fc, ys)\n",
- " xs_eff_lda = lda_eff.transform(xs_eff)\n",
- " xs_fc_lda = lda_fc.transform(xs_fc)\n",
- " return xs_eff_lda, xs_fc_lda, lda_eff, lda_fc\n",
- "\n",
- " dfs = []\n",
- "\n",
- " lda_eff = None\n",
- " lda_fc = None\n",
- "\n",
- " for step, model_path in tqdm(\n",
- " enumerate(model_paths),\n",
- " total=len(model_paths),\n",
- " desc=\"Extracting LDA\",\n",
- " ):\n",
- " # Extracts the Eff and FC layer\n",
- " # and the X scaler and Y encoder\n",
- " m_eff, m_fc, x_scaler, y_encoder = extract_model(model_path)\n",
- "\n",
- " # Preprocess the data\n",
- " (xs, ys), _xs_unl = preprocess(xs_raw, ys_raw, x_scaler, y_encoder)\n",
- "\n",
- " # Extract the LDA\n",
- " xs_eff_lda, xs_fc_lda, lda_eff, lda_fc = extract_lda(\n",
- " (xs_eff := m_eff(xs)).detach().numpy(),\n",
- " m_fc(xs_eff).detach().numpy(),\n",
- " ys,\n",
- " lda_eff=lda_eff,\n",
- " lda_fc=lda_fc,\n",
- " )\n",
- "\n",
- " # Construct the dataframe\n",
- " df = pd.DataFrame(xs_fc_lda, columns=[\"x\", \"y\"])\n",
- " df[\"label\"] = ys_raw\n",
- " df[\"label\"] = df[\"label\"].astype(\"category\")\n",
- " df[\"step\"] = step\n",
- " df[[\"orient\", \"sample\"]] = [(i % 8, i // 8) for i in range(len(df))]\n",
- " dfs.append(df)\n",
- " df = pd.concat(dfs, ignore_index=True)\n",
- " return df\n",
- "\n",
- "\n",
- "def plot_svm_boundaries(\n",
- " x,\n",
- " y,\n",
- " labels,\n",
- " xy_range=25,\n",
- " resolution=200,\n",
- " contour_opacity=0.5,\n",
- " width=800,\n",
- " height=800,\n",
- ") -> go.Figure:\n",
- " \"\"\"Plot the data points and the decision boundaries.\n",
- "\n",
- " Args:\n",
- " x: The x values of the data points.\n",
- " y: The y values of the data points.\n",
- " labels: The labels of the data points.\n",
- " xy_range: The range of the x and y axes.\n",
- " resolution: The resolution of the meshgrid.\n",
- " contour_opacity: The opacity of the decision boundaries.\n",
- " width: The width of the plot.\n",
- " height: The height of the plot.\n",
- "\n",
- " \"\"\"\n",
- " labels_int = (\n",
- " OrdinalEncoder()\n",
- " .fit_transform(np.array(labels).reshape(-1, 1))\n",
- " .flatten()\n",
- " )\n",
- " xy = np.c_[x, y]\n",
- " # Create a scatter plot\n",
- " fig = go.Figure(\n",
- " data=go.Scatter(\n",
- " x=x,\n",
- " y=y,\n",
- " mode=\"markers\",\n",
- " marker=dict(\n",
- " color=labels_int, # Use 'Jet' color scale\n",
- " size=10,\n",
- " colorscale=\"jet\",\n",
- " # Border\n",
- " line=dict(color=\"white\", width=1),\n",
- " ),\n",
- " # Add names as text\n",
- " text=labels,\n",
- " hovertemplate=\"%{text}\",\n",
- " ),\n",
- " )\n",
- "\n",
- " # For each class in ys_p, we retrieve the xs_lda values\n",
- " # then find the convex hull and draw it onto the plot\n",
- " for lab in np.unique(labels):\n",
- " xy_lab = xy[labels == lab]\n",
- " hull = ConvexHull(xy_lab)\n",
- " for simplex in hull.simplices:\n",
- " fig.add_trace(\n",
- " go.Scatter(\n",
- " x=xy_lab[simplex, 0],\n",
- " y=xy_lab[simplex, 1],\n",
- " mode=\"lines\",\n",
- " line=dict(color=\"white\"),\n",
- " hoverinfo=\"skip\", # No hover info\n",
- " ),\n",
- " )\n",
- "\n",
- " # Fit the SVC model\n",
- " clf = SVC()\n",
- " clf.fit(xy, labels_int)\n",
- "\n",
- " # Create a meshgrid for contour plot\n",
- " domain = np.linspace(-xy_range, xy_range, resolution)\n",
- " xx, yy = np.meshgrid(domain, domain)\n",
- " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
- " Z = Z.reshape(xx.shape)\n",
- "\n",
- " # Add contour plot to the figure with transparency\n",
- " fig.add_trace(\n",
- " go.Contour(\n",
- " x=domain,\n",
- " y=domain,\n",
- " z=Z,\n",
- " showscale=False,\n",
- " opacity=contour_opacity,\n",
- " colorscale=\"jet\",\n",
- " hoverinfo=\"skip\", # No hover info\n",
- " )\n",
- " )\n",
- "\n",
- " # Make the aspect ratio square\n",
- " fig.update_yaxes(scaleanchor=\"x\", scaleratio=1)\n",
- " fig.update_layout(showlegend=False, width=width, height=height)\n",
- " return fig\n",
- "\n",
- "\n",
- "def plot_anim(\n",
- " x, y, label, step, xy_range=25, width=800, height=800\n",
- ") -> go.Figure:\n",
- " \"\"\"Plot an animation of the data points.\n",
- "\n",
- " Args:\n",
- " x: The x values of the data points.\n",
- " y: The y values of the data points.\n",
- " label: The labels of the data points.\n",
- " step: The step of the animation.\n",
- " xy_range: The range of the x and y axes.\n",
- " width: The width of the plot.\n",
- " height: The height of the plot.\n",
- " \"\"\"\n",
- " fig = px.scatter(\n",
- " x=x,\n",
- " y=y,\n",
- " animation_frame=step,\n",
- " color=label,\n",
- " hover_name=label,\n",
- " range_x=[-xy_range, xy_range],\n",
- " range_y=[-xy_range, xy_range],\n",
- " )\n",
- " fig.update_layout(width=width, height=height)\n",
- " return fig\n"
+ "m = EfficientNetB1FixMatchModule.load_from_checkpoint(model_paths[-1]).eval()\n",
+ "m: EfficientNetB1FixMatchModule"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-06-04T04:53:08.555154Z",
- "start_time": "2024-06-04T04:53:05.678486700Z"
+ "end_time": "2024-06-04T09:40:04.084655200Z",
+ "start_time": "2024-06-04T09:40:03.556463900Z"
}
},
- "id": "2c65e7d2029dab4b",
+ "id": "8f43a473429a5e58",
"execution_count": 2
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "d36dce5890dba29c",
+ "outputs": [],
+ "source": [
+ "ds_chs = FRDCDatasetStaticEval(\n",
+ " \"chestnut_nature_park\",\n",
+ " \"20201218\",\n",
+ " None,\n",
+ " transform=val_preprocess(255),\n",
+ ")\n",
+ "x_chs_raw, y_chs_raw = list(zip(*ds_chs))\n",
+ "x_chs_raw = torch.stack(x_chs_raw).detach()\n",
+ "(x_chs, y_chs), _ = preprocess(\n",
+ " x_chs_raw, y_chs_raw, m.x_scaler, m.y_encoder\n",
+ ")\n",
+ "x_chs_emb = m.eff(x_chs)"
+ ],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-06-04T04:53:22.493880800Z",
- "start_time": "2024-06-04T04:53:08.558559200Z"
+ "end_time": "2024-06-04T09:40:13.377009700Z",
+ "start_time": "2024-06-04T09:40:04.089937100Z"
}
},
+ "id": "ac546548a044fa8b",
+ "execution_count": 3
+ },
+ {
+ "cell_type": "code",
"outputs": [],
"source": [
- "from model_tests.utils import FRDCDatasetStaticEval, val_preprocess\n",
- "import torch\n",
- "\n",
- "ds = FRDCDatasetStaticEval(\n",
- " \"chestnut_nature_park\", \"20201218\", None,\n",
+ "ds_cas = FRDCDatasetStaticEval(\n",
+ " \"casuarina\",\n",
+ " \"20220418\",\n",
+ " \"93deg\",\n",
" transform=val_preprocess(255),\n",
")\n",
- "xs_raw, ys_raw = list(zip(*ds))\n",
- "xs_raw = torch.stack(xs_raw)"
- ]
+ "x_cas_raw, y_cas_raw = list(zip(*ds_cas))\n",
+ "x_cas_raw = torch.stack(x_cas_raw).detach()\n",
+ "m.y_encoder.encoded_missing_value = -1\n",
+ "(x_cas, _), _ = preprocess(\n",
+ " x_cas_raw, y_cas_raw, m.x_scaler, m.y_encoder, nan_mask=False\n",
+ ")\n",
+ "x_cas_emb = m.eff(x_cas)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-06-04T09:40:37.637881100Z",
+ "start_time": "2024-06-04T09:40:13.379650300Z"
+ }
+ },
+ "id": "8e24199179540e21",
+ "execution_count": 4
},
{
"cell_type": "code",
- "execution_count": 4,
- "id": "ad591487ed18579b",
+ "outputs": [],
+ "source": [
+ "lda = LinearDiscriminantAnalysis(n_components=2)\n",
+ "lda.fit(x_chs_emb.numpy(), y_chs)\n",
+ "x_chs_lda = lda.transform(x_chs_emb.numpy())\n",
+ "x_cas_lda = lda.transform(x_cas_emb.numpy())"
+ ],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-06-04T04:53:50.135046600Z",
- "start_time": "2024-06-04T04:53:22.497162200Z"
+ "end_time": "2024-06-04T09:40:37.846233400Z",
+ "start_time": "2024-06-04T09:40:37.637881100Z"
}
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Extracting LDA: 100%|██████████| 6/6 [00:23<00:00, 3.95s/it]\n"
- ]
- }
+ "id": "941de67f0e9e68ca",
+ "execution_count": 5
+ },
+ {
+ "cell_type": "code",
+ "outputs": [],
+ "source": [
+ "df = pd.concat([\n",
+ " pd.DataFrame(x_chs_lda, columns=[\"x\", \"y\"])\n",
+ " .assign(label=y_chs_raw, site=\"chestnut_nature_park\"),\n",
+ " pd.DataFrame(x_cas_lda, columns=[\"x\", \"y\"])\n",
+ " .assign(label=y_cas_raw, site=\"casuarina\"),\n",
+ "]).astype({\"label\": \"category\"})\n",
+ "df[['orient', 'sample']] = [(i % 8, i // 8) for i in range(len(df))]"
],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "end_time": "2024-06-04T09:40:37.861506200Z",
+ "start_time": "2024-06-04T09:40:37.848361400Z"
+ }
+ },
+ "id": "adedbd77c4248168",
+ "execution_count": 6
+ },
+ {
+ "cell_type": "code",
+ "outputs": [],
"source": [
- "from tqdm import tqdm\n",
- "from frdc.train.utils import preprocess\n",
- "from frdc.models.efficientnetb1 import EfficientNetB1FixMatchModule\n",
- "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
- "\n",
- "import pandas as pd\n",
- "\n",
- "\n",
- "def extract_model(model_path):\n",
- " m = EfficientNetB1FixMatchModule.load_from_checkpoint(model_path).eval()\n",
- " m: EfficientNetB1FixMatchModule\n",
- " m_eff = m.eff\n",
- " m_fc = m.fc[0]\n",
- " return m_eff, m_fc, m.x_scaler, m.y_encoder\n",
- "\n",
- "\n",
- "def extract_lda(xs_eff, xs_fc, ys, lda_eff, lda_fc, n_components=2):\n",
- " if lda_eff is None:\n",
- " lda_eff = LinearDiscriminantAnalysis(n_components=n_components)\n",
- " lda_eff.fit(xs_eff, ys)\n",
- " if lda_fc is None:\n",
- " lda_fc = LinearDiscriminantAnalysis(n_components=n_components)\n",
- " lda_fc.fit(xs_fc, ys)\n",
- " xs_eff_lda = lda_eff.transform(xs_eff)\n",
- " xs_fc_lda = lda_fc.transform(xs_fc)\n",
- " return xs_eff_lda, xs_fc_lda, lda_eff, lda_fc\n",
- "\n",
- "\n",
- "dfs = []\n",
- "\n",
- "lda_eff = None\n",
- "lda_fc = None\n",
- "\n",
- "for step, model_path in tqdm(\n",
- " enumerate(model_paths),\n",
- " total=len(model_paths),\n",
- " desc=\"Extracting LDA\",\n",
- "):\n",
- " # Extracts the Eff and FC layer\n",
- " # and the X scaler and Y encoder\n",
- " m_eff, m_fc, x_scaler, y_encoder = extract_model(model_path)\n",
+ "import numpy as np\n",
"\n",
- " # Preprocess the data\n",
- " (xs, ys), _xs_unl = preprocess(xs_raw, ys_raw, x_scaler, y_encoder)\n",
"\n",
- " # Extract the LDA\n",
- " xs_eff_lda, xs_fc_lda, lda_eff, lda_fc = extract_lda(\n",
- " (xs_eff := m_eff(xs)).detach().numpy(),\n",
- " m_fc(xs_eff).detach().numpy(),\n",
- " ys,\n",
- " lda_eff=lda_eff,\n",
- " lda_fc=lda_fc,\n",
+ "def plot(df_: pd.DataFrame):\n",
+ " fig = px.scatter(\n",
+ " df_,\n",
+ " x=\"x\",\n",
+ " y=\"y\",\n",
+ " color=\"label\",\n",
+ " hover_name=\"label\",\n",
+ " range_x=[-12, 12],\n",
+ " range_y=[-12, 12],\n",
+ " # Different marker for different site\n",
+ " symbol=\"site\",\n",
" )\n",
+ " \n",
+ " has_legend = False\n",
+ " for lab, samp in df_[[\"label\", \"sample\"]].drop_duplicates().values:\n",
+ " xy_lab = df_[\n",
+ " (df_[\"label\"] == lab) & (df_[\"sample\"] == samp)\n",
+ " ][[\"x\", \"y\"]].values\n",
+ " hull = ConvexHull(xy_lab)\n",
+ " for simplex in hull.simplices:\n",
+ " fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=xy_lab[simplex, 0],\n",
+ " y=xy_lab[simplex, 1],\n",
+ " mode=\"lines\",\n",
+ " line=dict(color=\"white\", width=1),\n",
+ " hoverinfo=\"skip\",\n",
+ " showlegend=False if has_legend else True,\n",
+ " legendgroup=\"Sample Convex Hull\",\n",
+ " name=\"Sample Convex Hull\",\n",
+ " ),\n",
+ " )\n",
+ " has_legend = True\n",
+ " \n",
+ " has_legend = False\n",
+ " for lab in np.unique(df_[\"label\"]):\n",
+ " xy_lab = df_[df_[\"label\"] == lab][[\"x\", \"y\"]].values \n",
+ " hull = ConvexHull(xy_lab)\n",
+ " for simplex in hull.simplices:\n",
+ " fig.add_trace(\n",
+ " go.Scatter(\n",
+ " x=xy_lab[simplex, 0],\n",
+ " y=xy_lab[simplex, 1],\n",
+ " mode=\"lines\",\n",
+ " line=dict(color=\"white\", width=3),\n",
+ " hoverinfo=\"skip\",\n",
+ " showlegend=False if has_legend else True,\n",
+ " legendgroup=\"Species Convex Hull\",\n",
+ " name=\"Species Convex Hull\",\n",
+ " ),\n",
+ " )\n",
+ " has_legend = True\n",
"\n",
- " # Construct the dataframe\n",
- " df = pd.DataFrame(xs_fc_lda, columns=['x', 'y'])\n",
- " df['label'] = ys_raw\n",
- " df['label'] = df['label'].astype('category')\n",
- " df['step'] = step\n",
- " df[['orient', 'sample']] = [(i % 8, i // 8) for i in range(len(df))]\n",
- " dfs.append(df)\n",
- "df = pd.concat(dfs, ignore_index=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "478f6eb2a9eaf3c4",
+ " fig.update_layout(width=1200, height=800)\n",
+ " fig.show()"
+ ],
"metadata": {
"collapsed": false,
"ExecuteTime": {
- "end_time": "2024-06-04T04:53:50.154188800Z",
- "start_time": "2024-06-04T04:53:50.133624400Z"
+ "end_time": "2024-06-04T09:40:37.867148Z",
+ "start_time": "2024-06-04T09:40:37.860048800Z"
}
},
- "outputs": [
- {
- "data": {
- "text/plain": " x y label step orient sample\n0 -6.896686 -3.340312 Falcataria Moluccana 0 0 0\n1 -6.009223 -3.940270 Falcataria Moluccana 0 1 0\n2 -4.735628 -1.184250 Falcataria Moluccana 0 2 0\n3 -6.142573 -3.102567 Falcataria Moluccana 0 3 0\n4 -6.750273 -2.793355 Falcataria Moluccana 0 4 0\n... ... ... ... ... ... ...\n1531 -0.068610 3.011702 Alstonia Angulstiloba 5 3 31\n1532 1.226064 1.432768 Alstonia Angulstiloba 5 4 31\n1533 -0.517754 3.480461 Alstonia Angulstiloba 5 5 31\n1534 0.238426 4.833471 Alstonia Angulstiloba 5 6 31\n1535 -1.051879 5.066136 Alstonia Angulstiloba 5 7 31\n\n[1536 rows x 6 columns]",
- "text/html": "
\n\n
\n \n \n | \n x | \n y | \n label | \n step | \n orient | \n sample | \n
\n \n \n \n 0 | \n -6.896686 | \n -3.340312 | \n Falcataria Moluccana | \n 0 | \n 0 | \n 0 | \n
\n \n 1 | \n -6.009223 | \n -3.940270 | \n Falcataria Moluccana | \n 0 | \n 1 | \n 0 | \n
\n \n 2 | \n -4.735628 | \n -1.184250 | \n Falcataria Moluccana | \n 0 | \n 2 | \n 0 | \n
\n \n 3 | \n -6.142573 | \n -3.102567 | \n Falcataria Moluccana | \n 0 | \n 3 | \n 0 | \n
\n \n 4 | \n -6.750273 | \n -2.793355 | \n Falcataria Moluccana | \n 0 | \n 4 | \n 0 | \n
\n \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n ... | \n
\n \n 1531 | \n -0.068610 | \n 3.011702 | \n Alstonia Angulstiloba | \n 5 | \n 3 | \n 31 | \n
\n \n 1532 | \n 1.226064 | \n 1.432768 | \n Alstonia Angulstiloba | \n 5 | \n 4 | \n 31 | \n
\n \n 1533 | \n -0.517754 | \n 3.480461 | \n Alstonia Angulstiloba | \n 5 | \n 5 | \n 31 | \n
\n \n 1534 | \n 0.238426 | \n 4.833471 | \n Alstonia Angulstiloba | \n 5 | \n 6 | \n 31 | \n
\n \n 1535 | \n -1.051879 | \n 5.066136 | \n Alstonia Angulstiloba | \n 5 | \n 7 | \n 31 | \n
\n \n
\n
1536 rows × 6 columns
\n
"
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df"
- ]
+ "id": "6716a6b49801f91c",
+ "execution_count": 7
},
{
"cell_type": "markdown",
- "id": "476315d917e28669",
+ "source": [
+ "# Plots \n",
+ "\n",
+ "## Interpretation\n",
+ "The \"dots\" are simply the embedding of each sample/instance.\n",
+ "Not\n",
+ "\n",
+ "## Chestnut"
+ ],
"metadata": {
"collapsed": false
},
- "source": [
- "# Plots"
- ]
+ "id": "2cda55b02e4cf6f6"
},
{
"cell_type": "code",
- "execution_count": 7,
- "id": "1376f2c23cf7041f",
- "metadata": {
- "collapsed": false,
- "ExecuteTime": {
- "end_time": "2024-06-04T04:54:05.728691Z",
- "start_time": "2024-06-04T04:54:04.446003100Z"
- }
- },
"outputs": [
{
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label=Falcataria Moluccana
site=chestnut_nature_park
x=%{x}
y=%{y}",
+ "hovertext": [
"Falcataria Moluccana",
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"Falcataria Moluccana",
@@ -723,6 +279,91 @@
"Falcataria Moluccana",
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"Falcataria Moluccana",
+ "Falcataria Moluccana",
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site=chestnut_nature_park
x=%{x}
y=%{y}",
+ "hovertext": [
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"Claoxylon Indicum",
"Claoxylon Indicum",
@@ -730,8 +371,44 @@
"Claoxylon Indicum",
"Claoxylon Indicum",
"Claoxylon Indicum",
- "Claoxylon Indicum",
- "Pennisetum Purpureum",
+ "Claoxylon Indicum"
+ ],
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+ "color": "#EF553B",
+ "symbol": "circle"
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x=%{x}
y=%{y}",
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"Pennisetum Purpureum",
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"Pennisetum Purpureum",
@@ -739,6 +416,44 @@
"Pennisetum Purpureum",
"Pennisetum Purpureum",
"Pennisetum Purpureum",
+ "Pennisetum Purpureum"
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+ "legendgroup": "Pennisetum Purpureum, chestnut_nature_park",
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site=chestnut_nature_park
x=%{x}
y=%{y}",
+ "hovertext": [
"Bridelia Sp.",
"Bridelia Sp.",
"Bridelia Sp.",
@@ -747,15 +462,160 @@
"Bridelia Sp.",
"Bridelia Sp.",
"Bridelia Sp.",
+ "Bridelia Sp.",
+ "Bridelia Sp.",
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x=%{x}
y=%{y}",
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"Pometia Pinnata",
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"Leea Indica",
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"Sandoricum Koetjape",
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"Syzygium Polyanthum",
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"Ficus Variegata",
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"Cinnamomum Iners",
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diff --git a/src/frdc/train/utils.py b/src/frdc/train/utils.py
index ee6c88a..6e85fe7 100644
--- a/src/frdc/train/utils.py
+++ b/src/frdc/train/utils.py
@@ -58,6 +58,7 @@ def preprocess(
x_scaler: StandardScaler,
y_encoder: OrdinalEncoder,
x_unl: list[torch.Tensor] = None,
+ nan_mask: bool = True,
) -> tuple[tuple[torch.Tensor, torch.Tensor], list[torch.Tensor]]:
"""Preprocesses the data
@@ -73,6 +74,8 @@ def preprocess(
y_lbl: The labels to preprocess.
x_scaler: The StandardScaler to use.
y_encoder: The OrdinalEncoder to use.
+ x_unl: The unlabelled data to preprocess.
+ nan_mask: Whether to remove nan values from the batch.
Returns:
The preprocessed data and labels.
@@ -92,7 +95,9 @@ def preprocess(
# Remove nan values from the batch
# Ordinal Encoders can return a np.nan if the value is not in the
# categories. We will remove that from the batch.
- nan = ~torch.isnan(y_trans)
+ nan = (
+ ~torch.isnan(y_trans) if nan_mask else torch.ones_like(y_trans).bool()
+ )
x_lbl_trans = x_lbl_trans[nan]
x_lbl_trans = torch.nan_to_num(x_lbl_trans)
x_unl_trans = fn_recursive(