update pandas

SUNCAT-Center · Mar 2, 2020 · fe46f9c · fe46f9c
1 parent bef4f7e
commit fe46f9c
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Showing 3 changed files with 20 additions and 70 deletions.
diff --git a/catlearn/featurize/setup.py b/catlearn/featurize/setup.py
@@ -136,7 +136,7 @@ def featurize_atomic_pairs(self, candidates):
  for atoms in tqdm(candidates):
  # One hot encode elements.
  dummies = pd.get_dummies(atoms.numbers)
- d = dummies.T.reindex(self.atom_types).fillna(0).T.as_matrix()
+ d = dummies.T.reindex(self.atom_types).fillna(0).T.values
 
  # Number of pairs by 1 + number of element types data matrix.
  n_pairs = len(atoms) ** 2
@@ -272,9 +272,9 @@ def _concatenate_vec(self, atoms, vec_names):
  fingerprint_vector.dtype.type is not np.str_) or
  (arr.dtype.type is not np.str_ and
  fingerprint_vector.dtype.type is np.str_)):
-  raise AssertionError("Fingerprints should be float" +
-  " type, and returned separately" +
-  " from str types.")
+ raise AssertionError("Fingerprints should be float" +
+ " type, and returned separately" +
+ " from str types.")
  fingerprint_vector = np.concatenate((fingerprint_vector,
  arr))
 

diff --git a/tutorials/09_bulk_fingerprints/Voronoi_FP.ipynb b/tutorials/09_bulk_fingerprints/Voronoi_FP.ipynb
@@ -29,10 +29,8 @@
  },
  {
  "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": null,
+ "metadata": {},
  "outputs": [],
  "source": [
  "# Import packages.\n",
@@ -52,17 +50,9 @@
  },
  {
  "cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
  "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "pulled 10 molecules from db\n"
- ]
- }
- ],
+ "outputs": [],
  "source": [
  "# Connect the ase-db.\n",
  "db = ase.db.connect('../../data/cubic_perovskites.db')\n",
@@ -81,17 +71,9 @@
  },
  {
  "cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
  "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "min: 5, mean: 5, max: 5 atoms size\n"
- ]
- }
- ],
+ "outputs": [],
  "source": [
  "# Check the size of the atomic strucures.\n",
  "size = []\n",
@@ -111,17 +93,9 @@
  },
  {
  "cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
  "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generate Voronoi fingerprint of 10 structures\n"
- ]
- }
- ],
+ "outputs": [],
  "source": [
  "voro = VoronoiFingerprintGenerator(alist)\n",
  "data_frame = voro.generate()"
@@ -139,27 +113,11 @@
  },
  {
  "cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
  "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(10, 271)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/raul_desktop/anaconda2/envs/py36/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
- " \"\"\"Entry point for launching an IPython kernel.\n"
- ]
- }
- ],
+ "outputs": [],
  "source": [
- "matrix = data_frame.as_matrix()\n",
+ "matrix = data_frame.values\n",
  "finite_numeric_data = clean_infinite(matrix)\n",
  "print(np.shape(finite_numeric_data['train']))"
  ]
@@ -173,17 +131,9 @@
  },
  {
  "cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
  "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(10, 215)\n"
- ]
- }
- ],
+ "outputs": [],
  "source": [
  "useful_data = clean_variance(finite_numeric_data['train'])\n",
  "print(np.shape(useful_data['train']))"
@@ -213,9 +163,9 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.6.7"
+ "version": "3.7.4"
  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/tutorials/09_bulk_fingerprints/Voronoi_FP.py b/tutorials/09_bulk_fingerprints/Voronoi_FP.py
@@ -83,7 +83,7 @@
 # In[5]:
 
 
-matrix = data_frame.as_matrix()
+matrix = data_frame.values
 finite_numeric_data = clean_infinite(matrix)
 print(np.shape(finite_numeric_data['train']))