From 94e62f95e9359aa13702f7c07319996408f58c23 Mon Sep 17 00:00:00 2001 From: Fabian Zills Date: Wed, 4 Dec 2024 21:11:01 +0100 Subject: [PATCH 1/5] add plotly figures --- apax/nodes/selection.py | 169 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 167 insertions(+), 2 deletions(-) diff --git a/apax/nodes/selection.py b/apax/nodes/selection.py index 7e40cc8b..75a80471 100644 --- a/apax/nodes/selection.py +++ b/apax/nodes/selection.py @@ -7,13 +7,17 @@ import zntrack.utils from matplotlib import pyplot as plt +import plotly.graph_objects as go +import plotly.io as pio + +from apax.bal import kernel_selection +from apax.nodes.model import ApaxBase + try: from sklearn.decomposition import PCA except ImportError: PCA = None -from apax.bal import kernel_selection -from apax.nodes.model import ApaxBase log = logging.getLogger(__name__) @@ -106,6 +110,10 @@ class BatchKernelSelection(zntrack.Node): img_distances: Path = zntrack.outs_path(zntrack.nwd / "distances.png") img_features: Path = zntrack.outs_path(zntrack.nwd / "features.png") + pio_selection: Path = zntrack.outs_path(zntrack.nwd / "selection.json") + pio_distances: Path = zntrack.outs_path(zntrack.nwd / "distances.json") + pio_features: Path = zntrack.outs_path(zntrack.nwd / "features.json") + def get_data(self) -> list[ase.Atoms]: """Get the atoms data to process.""" if self.data is not None: @@ -263,3 +271,160 @@ def _get_pca_plot( ax.legend() fig.savefig(self.img_features, bbox_inches="tight", dpi=240) + + + + + + def _get_plotly_selection_plot(self, atoms_lst, indices): + energies = np.array([atoms.calc.results["energy"] for atoms in atoms_lst]) + + if "energy_uncertainty" in atoms_lst[0].calc.results.keys(): + uncertainty = np.array( + [atoms.calc.results["energy_uncertainty"] for atoms in atoms_lst] + ) + fig = go.Figure() + fig.add_trace( + go.Scatter( + y=energies, + mode="lines", + name="Energy", + ) + ) + fig.add_trace( + go.Scatter( + y=energies + uncertainty, + mode="lines", + line=dict(dash="dash"), + name="Energy + Uncertainty", + ) + ) + fig.add_trace( + go.Scatter( + y=energies - uncertainty, + mode="lines", + line=dict(dash="dash"), + name="Energy - Uncertainty", + ) + ) + else: + fig = go.Figure() + fig.add_trace( + go.Scatter( + y=energies, + mode="lines", + name="Energy", + ) + ) + + fig.add_trace( + go.Scatter( + x=indices, + y=energies[indices], + mode="markers", + marker=dict(color="red", size=10), + name="Selected Configurations", + ) + ) + + fig.update_layout( + title="Energy Plot", + xaxis_title="Configuration", + yaxis_title="Energy", + ) + pio.write_json(fig, self.pio_selection) + + def _get_plotly_distances_plot(self, distances, last_selected): + fig = go.Figure() + fig.add_trace( + go.Scatter( + y=distances, + mode="lines", + name="Squared Distances", + ) + ) + fig.add_vline( + x=last_selected, + line=dict(dash="dash", color="gray"), + annotation_text="Last Selected", + ) + + fig.update_layout( + title="Squared Distances Plot", + xaxis_title="Configuration", + yaxis_title="Squared Distance (Log Scale)", + yaxis_type="log", + ) + pio.write_json(fig, self.pio_distances) + + def _get_plotly_pca_plot(self, g_train, g_selection, g_remaining): + if PCA: + all_features = [g_train, g_selection] + if len(g_remaining) > 0: + all_features.append(g_remaining) + g_full = np.concatenate(all_features, axis=0) + pca = PCA(n_components=2) + pca.fit(g_full) + + g_train_2d = pca.transform(g_train) + g_selection_2d = pca.transform(g_selection) + if len(g_remaining) > 0: + g_pool_2d = pca.transform(g_remaining) + else: + F = g_train.shape[1] + W = np.random.randn(F, 2) / np.sqrt(F) + g_train_2d = g_train @ W + g_selection_2d = g_selection @ W + if len(g_remaining) > 0: + g_pool_2d = g_remaining @ W + + fig = go.Figure() + + if len(g_remaining) > 0: + fig.add_trace( + go.Scatter( + x=g_pool_2d[:, 0], + y=g_pool_2d[:, 1], + mode="markers", + marker=dict(color="gray", size=5, opacity=0.6), + name="Remaining Data", + ) + ) + + fig.add_trace( + go.Scatter( + x=g_train_2d[:, 0], + y=g_train_2d[:, 1], + mode="markers", + marker=dict(color="blue", size=5, symbol="triangle-up", opacity=0.6), + name="Train Data", + ) + ) + fig.add_trace( + go.Scatter( + x=g_selection_2d[:, 0], + y=g_selection_2d[:, 1], + mode="markers", + marker=dict(color="red", size=10, symbol="star", opacity=0.6), + name="Selected Data", + ) + ) + + fig.update_layout( + title="PCA Projection", + xaxis_title="Dimension 1", + yaxis_title="Dimension 2", + legend=dict(title="Data Type"), + ) + pio.write_json(fig, self.pio_features) + + @property + def figures(self) -> dict[str, go.Figure]: + """Get the plotly figures.""" + with self.state.fs.open(self.pio_selection) as f: + selection = pio.read_json(f) + with self.state.fs.open(self.pio_distances) as f: + distances = pio.read_json(f) + with self.state.fs.open(self.pio_features) as f: + features = pio.read_json(f) + return {"selection": selection, "distances": distances, "features": features} From 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plotly.graph_objects as go import plotly.io as pio +import zntrack.utils +from matplotlib import pyplot as plt from apax.bal import kernel_selection from apax.nodes.model import ApaxBase From 0d82574a03ddd327a4e6d2d64c87a8017dec7538 Mon Sep 17 00:00:00 2001 From: Fabian Zills Date: Wed, 4 Dec 2024 21:13:39 +0100 Subject: [PATCH 4/5] bump version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 9b344192..021adc69 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "apax" -version = "0.9.1" +version = "0.9.2" description = "Atomistic Learned Potential Package in JAX" authors = ["Moritz René Schäfer ", "Nico Segreto "] keywords=["machine-learning", "interatomic potentials", "molecular-dynamics"] From 43feea560d42c386fba622ddad3a6b78815a0c42 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Moritz=20R=2E=20Sch=C3=A4fer?= Date: Mon, 20 Jan 2025 13:26:56 +0100 Subject: [PATCH 5/5] switch to uv 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